diff --git a/ci/envs/310-latest.yaml b/ci/envs/310-latest.yaml
index b6eec1e5..709d4a81 100644
--- a/ci/envs/310-latest.yaml
+++ b/ci/envs/310-latest.yaml
@@ -14,6 +14,8 @@ dependencies:
   - esda
   - tqdm
   - numba
+  - rioxarray
+  - xvec
   # testing
   - codecov
   - pytest
diff --git a/ci/envs/311-latest.yaml b/ci/envs/311-latest.yaml
index 186b8fcb..4d4119b5 100644
--- a/ci/envs/311-latest.yaml
+++ b/ci/envs/311-latest.yaml
@@ -14,6 +14,8 @@ dependencies:
   - esda
   - tqdm
   - numba
+  - rioxarray
+  - xvec
   # testing
   - codecov
   - pytest
diff --git a/ci/envs/312-latest.yaml b/ci/envs/312-latest.yaml
index 2a9b2670..0de5f258 100644
--- a/ci/envs/312-latest.yaml
+++ b/ci/envs/312-latest.yaml
@@ -15,6 +15,8 @@ dependencies:
   - esda
   - tqdm
   - numba
+  - rioxarray
+  - xvec
   # testing
   - codecov
   - pytest
diff --git a/docs/api.rst b/docs/api.rst
index 043c91dc..ec89de5c 100644
--- a/docs/api.rst
+++ b/docs/api.rst
@@ -179,6 +179,17 @@ With utilities allowing conversion between networkx objects and GeoPandas object
    gdf_to_nx
    nx_to_gdf
 
+
+Measuring streetscape
+---------------------
+
+Specialised class for the advanced streetscape analysis.
+
+.. autosummary::
+   :toctree: api/
+
+   Streetscape
+
 Data preprocessing
 ------------------
 
diff --git a/docs/examples/streetscape.ipynb b/docs/examples/streetscape.ipynb
new file mode 100644
index 00000000..998e5bda
--- /dev/null
+++ b/docs/examples/streetscape.ipynb
@@ -0,0 +1,871 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Example of using the `Streetscape` class"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/martin/dev/pysal/.pixi/envs/default/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
+   "source": [
+    "import geopandas as gpd\n",
+    "import momepy\n",
+    "import numpy as np\n",
+    "import rioxarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read all the data. Only streets and buildings are required."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "streets = gpd.read_file(\n",
+    "    momepy.datasets.get_path(\"bubenec\"), layer=\"streets\"\n",
+    ").to_crs(5514)\n",
+    "buildings = gpd.read_file(\n",
+    "    momepy.datasets.get_path(\"bubenec\"), layer=\"buildings\"\n",
+    ").to_crs(5514)\n",
+    "plots = gpd.read_file(\n",
+    "    momepy.datasets.get_path(\"bubenec\"), layer=\"plots\"\n",
+    ").to_crs(5514)\n",
+    "dtm = rioxarray.open_rasterio(momepy.datasets.get_path(\"bubenec\"), layer=\"dtm\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mimic data on building category and height."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buildings[\"category\"] = np.random.randint(0, 6, len(buildings))\n",
+    "buildings[\"height\"] = np.random.randint(12, 30, len(buildings))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Initiate the class. This will dricectly compute builk of the sightline indicators based on streets and buildings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = momepy.Streetscape(\n",
+    "    streets, buildings, category_col=\"category\", height_col=\"height\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have plots and DTM, you can use two additional methods to compute additional variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.compute_plots(plots)\n",
+    "sc.compute_slope(dtm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting data can be extracted either on a street level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>n_l</th>\n",
+       "      <th>n_r</th>\n",
+       "      <th>left_os</th>\n",
+       "      <th>right_os</th>\n",
+       "      <th>os</th>\n",
+       "      <th>left_os_std</th>\n",
+       "      <th>right_os_std</th>\n",
+       "      <th>os_std</th>\n",
+       "      <th>left_os_mad</th>\n",
+       "      <th>...</th>\n",
+       "      <th>left_plot_WD_ratio</th>\n",
+       "      <th>right_plot_WD_ratio</th>\n",
+       "      <th>plot_WP_ratio</th>\n",
+       "      <th>left_plot_WP_ratio</th>\n",
+       "      <th>right_plot_WP_ratio</th>\n",
+       "      <th>slope_degree</th>\n",
+       "      <th>slope_percent</th>\n",
+       "      <th>n_slopes</th>\n",
+       "      <th>slope_valid</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>street_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>57</td>\n",
+       "      <td>28</td>\n",
+       "      <td>38</td>\n",
+       "      <td>33.607135</td>\n",
+       "      <td>38.163914</td>\n",
+       "      <td>71.771049</td>\n",
+       "      <td>14.794107</td>\n",
+       "      <td>12.059253</td>\n",
+       "      <td>13.436283</td>\n",
+       "      <td>13.873261</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.156827</td>\n",
+       "      <td>1.765917</td>\n",
+       "      <td>0.170662</td>\n",
+       "      <td>0.156054</td>\n",
+       "      <td>0.184501</td>\n",
+       "      <td>2.809318</td>\n",
+       "      <td>0.049073</td>\n",
+       "      <td>57</td>\n",
+       "      <td>True</td>\n",
+       "      <td>LINESTRING (-743681.992 -1040957.169, -743809....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>22</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>17.497487</td>\n",
+       "      <td>67.497487</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.015687</td>\n",
+       "      <td>9.794670</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.440014</td>\n",
+       "      <td>1.037320</td>\n",
+       "      <td>0.255912</td>\n",
+       "      <td>0.284263</td>\n",
+       "      <td>0.230261</td>\n",
+       "      <td>0.166605</td>\n",
+       "      <td>0.002908</td>\n",
+       "      <td>22</td>\n",
+       "      <td>True</td>\n",
+       "      <td>LINESTRING (-743916.081 -1041162.952, -743899....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>43</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21</td>\n",
+       "      <td>13.907200</td>\n",
+       "      <td>35.792031</td>\n",
+       "      <td>49.699231</td>\n",
+       "      <td>16.207022</td>\n",
+       "      <td>15.188099</td>\n",
+       "      <td>15.613165</td>\n",
+       "      <td>11.913767</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.822465</td>\n",
+       "      <td>1.635065</td>\n",
+       "      <td>0.200568</td>\n",
+       "      <td>0.221176</td>\n",
+       "      <td>0.181546</td>\n",
+       "      <td>1.122029</td>\n",
+       "      <td>0.019587</td>\n",
+       "      <td>43</td>\n",
+       "      <td>True</td>\n",
+       "      <td>LINESTRING (-743689.806 -1041115.822, -743698....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>48.865531</td>\n",
+       "      <td>98.865531</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.101247</td>\n",
+       "      <td>2.869002</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.444666</td>\n",
+       "      <td>1.307638</td>\n",
+       "      <td>0.156703</td>\n",
+       "      <td>0.060853</td>\n",
+       "      <td>0.248559</td>\n",
+       "      <td>2.735747</td>\n",
+       "      <td>0.047794</td>\n",
+       "      <td>24</td>\n",
+       "      <td>True</td>\n",
+       "      <td>LINESTRING (-743618.342 -1040934.607, -743621....</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.023257</td>\n",
+       "      <td>0.842462</td>\n",
+       "      <td>0.068937</td>\n",
+       "      <td>0.050164</td>\n",
+       "      <td>0.058499</td>\n",
+       "      <td>1.505840</td>\n",
+       "      <td>0.026294</td>\n",
+       "      <td>15</td>\n",
+       "      <td>True</td>\n",
+       "      <td>LINESTRING (-743701.515 -1040870.813, -743693....</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 102 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               N  n_l  n_r    left_os   right_os          os  left_os_std  \\\n",
+       "street_index                                                                \n",
+       "0             57   28   38  33.607135  38.163914   71.771049    14.794107   \n",
+       "1             22    0   19  50.000000  17.497487   67.497487     0.000000   \n",
+       "2             43   36   21  13.907200  35.792031   49.699231    16.207022   \n",
+       "3             24    0    0  50.000000  48.865531   98.865531     0.000000   \n",
+       "4             15    0    0  50.000000  50.000000  100.000000     0.000000   \n",
+       "\n",
+       "              right_os_std     os_std  left_os_mad  ...  left_plot_WD_ratio  \\\n",
+       "street_index                                        ...                       \n",
+       "0                12.059253  13.436283    13.873261  ...            1.156827   \n",
+       "1                14.015687   9.794670     0.000000  ...            1.440014   \n",
+       "2                15.188099  15.613165    11.913767  ...            0.822465   \n",
+       "3                 4.101247   2.869002     0.000000  ...            1.444666   \n",
+       "4                 0.000000   0.000000     0.000000  ...            1.023257   \n",
+       "\n",
+       "              right_plot_WD_ratio  plot_WP_ratio  left_plot_WP_ratio  \\\n",
+       "street_index                                                           \n",
+       "0                        1.765917       0.170662            0.156054   \n",
+       "1                        1.037320       0.255912            0.284263   \n",
+       "2                        1.635065       0.200568            0.221176   \n",
+       "3                        1.307638       0.156703            0.060853   \n",
+       "4                        0.842462       0.068937            0.050164   \n",
+       "\n",
+       "              right_plot_WP_ratio  slope_degree  slope_percent  n_slopes  \\\n",
+       "street_index                                                               \n",
+       "0                        0.184501      2.809318       0.049073        57   \n",
+       "1                        0.230261      0.166605       0.002908        22   \n",
+       "2                        0.181546      1.122029       0.019587        43   \n",
+       "3                        0.248559      2.735747       0.047794        24   \n",
+       "4                        0.058499      1.505840       0.026294        15   \n",
+       "\n",
+       "              slope_valid                                           geometry  \n",
+       "street_index                                                                  \n",
+       "0                    True  LINESTRING (-743681.992 -1040957.169, -743809....  \n",
+       "1                    True  LINESTRING (-743916.081 -1041162.952, -743899....  \n",
+       "2                    True  LINESTRING (-743689.806 -1041115.822, -743698....  \n",
+       "3                    True  LINESTRING (-743618.342 -1040934.607, -743621....  \n",
+       "4                    True  LINESTRING (-743701.515 -1040870.813, -743693....  \n",
+       "\n",
+       "[5 rows x 102 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "street_df = sc.street_level()\n",
+    "street_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is a GeoDataFrame, so you can directly plot it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = street_df.plot(\"os\", figsize=(12, 12), legend=True)\n",
+    "buildings.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or for all individual sightline points."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>geometry</th>\n",
+       "      <th>left_os_count</th>\n",
+       "      <th>left_os</th>\n",
+       "      <th>left_sb_count</th>\n",
+       "      <th>left_sb</th>\n",
+       "      <th>left_h</th>\n",
+       "      <th>left_hw</th>\n",
+       "      <th>left_bc</th>\n",
+       "      <th>right_os_count</th>\n",
+       "      <th>right_os</th>\n",
+       "      <th>...</th>\n",
+       "      <th>right_plot_seq_sb_depth</th>\n",
+       "      <th>os_count</th>\n",
+       "      <th>os</th>\n",
+       "      <th>sb_count</th>\n",
+       "      <th>sb</th>\n",
+       "      <th>h</th>\n",
+       "      <th>hw</th>\n",
+       "      <th>bc</th>\n",
+       "      <th>plot_seq_sb</th>\n",
+       "      <th>plot_seq_sb_depth</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>street_index</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743682.367 -1040957.5)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.240118</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.568200</td>\n",
+       "      <td>19.240118</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743684.627 -1040959.485)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.011884</td>\n",
+       "      <td>2</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.090066</td>\n",
+       "      <td>18.011884</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743686.886 -1040961.471)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.593446</td>\n",
+       "      <td>...</td>\n",
+       "      <td>15.658901</td>\n",
+       "      <td>2</td>\n",
+       "      <td>38.796723</td>\n",
+       "      <td>1</td>\n",
+       "      <td>27.593446</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.869772</td>\n",
+       "      <td>2.491801</td>\n",
+       "      <td>10.100149</td>\n",
+       "      <td>15.658901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743689.145 -1040963.457)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.229691</td>\n",
+       "      <td>...</td>\n",
+       "      <td>14.184017</td>\n",
+       "      <td>2</td>\n",
+       "      <td>38.114846</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.229691</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.914994</td>\n",
+       "      <td>5.604209</td>\n",
+       "      <td>9.996735</td>\n",
+       "      <td>27.216748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743691.405 -1040965.443)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25.412462</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.317758</td>\n",
+       "      <td>2</td>\n",
+       "      <td>37.706231</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25.412462</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.944419</td>\n",
+       "      <td>6.825341</td>\n",
+       "      <td>9.996961</td>\n",
+       "      <td>32.570991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743693.664 -1040967.428)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>23.978541</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.557431</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25.198986</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21.500873</td>\n",
+       "      <td>2</td>\n",
+       "      <td>24.588764</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25.198986</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.952419</td>\n",
+       "      <td>11.811294</td>\n",
+       "      <td>9.997186</td>\n",
+       "      <td>29.171224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743695.923 -1040969.414)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.720375</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15.763951</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.764536</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.928554</td>\n",
+       "      <td>2</td>\n",
+       "      <td>20.742455</td>\n",
+       "      <td>1</td>\n",
+       "      <td>26.764536</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.896709</td>\n",
+       "      <td>16.722367</td>\n",
+       "      <td>9.997412</td>\n",
+       "      <td>25.070210</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743698.182 -1040971.4)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.705134</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>16.541363</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28.184169</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.024346</td>\n",
+       "      <td>2</td>\n",
+       "      <td>21.444652</td>\n",
+       "      <td>1</td>\n",
+       "      <td>28.184169</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.851542</td>\n",
+       "      <td>15.669026</td>\n",
+       "      <td>9.997637</td>\n",
+       "      <td>21.235336</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743700.442 -1040973.386)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.689893</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.812513</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29.961410</td>\n",
+       "      <td>...</td>\n",
+       "      <td>19.832787</td>\n",
+       "      <td>2</td>\n",
+       "      <td>22.325652</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29.961410</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.801030</td>\n",
+       "      <td>13.593521</td>\n",
+       "      <td>9.997863</td>\n",
+       "      <td>24.073478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>POINT (-743702.701 -1040975.371)</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.674652</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19.807911</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31.357056</td>\n",
+       "      <td>...</td>\n",
+       "      <td>20.430319</td>\n",
+       "      <td>2</td>\n",
+       "      <td>23.015854</td>\n",
+       "      <td>1</td>\n",
+       "      <td>31.357056</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0.765378</td>\n",
+       "      <td>13.355549</td>\n",
+       "      <td>9.998088</td>\n",
+       "      <td>25.161555</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 30 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      geometry  left_os_count    left_os  \\\n",
+       "street_index                                                               \n",
+       "0               POINT (-743682.367 -1040957.5)              1  50.000000   \n",
+       "0             POINT (-743684.627 -1040959.485)              1  50.000000   \n",
+       "0             POINT (-743686.886 -1040961.471)              1  50.000000   \n",
+       "0             POINT (-743689.145 -1040963.457)              1  50.000000   \n",
+       "0             POINT (-743691.405 -1040965.443)              1  50.000000   \n",
+       "0             POINT (-743693.664 -1040967.428)              1  23.978541   \n",
+       "0             POINT (-743695.923 -1040969.414)              1  14.720375   \n",
+       "0               POINT (-743698.182 -1040971.4)              1  14.705134   \n",
+       "0             POINT (-743700.442 -1040973.386)              1  14.689893   \n",
+       "0             POINT (-743702.701 -1040975.371)              1  14.674652   \n",
+       "\n",
+       "              left_sb_count  left_sb  left_h  left_hw    left_bc  \\\n",
+       "street_index                                                       \n",
+       "0                         0      NaN     NaN      NaN   0.000000   \n",
+       "0                         0      NaN     NaN      NaN   0.000000   \n",
+       "0                         0      NaN     NaN      NaN   0.000000   \n",
+       "0                         0      NaN     NaN      NaN   0.000000   \n",
+       "0                         0      NaN     NaN      NaN   0.000000   \n",
+       "0                         0      NaN     NaN      NaN   6.557431   \n",
+       "0                         0      NaN     NaN      NaN  15.763951   \n",
+       "0                         0      NaN     NaN      NaN  16.541363   \n",
+       "0                         0      NaN     NaN      NaN  19.812513   \n",
+       "0                         0      NaN     NaN      NaN  19.807911   \n",
+       "\n",
+       "              right_os_count   right_os  ...  right_plot_seq_sb_depth  \\\n",
+       "street_index                             ...                            \n",
+       "0                          1  50.000000  ...                19.240118   \n",
+       "0                          1  50.000000  ...                18.011884   \n",
+       "0                          1  27.593446  ...                15.658901   \n",
+       "0                          1  26.229691  ...                14.184017   \n",
+       "0                          1  25.412462  ...                23.317758   \n",
+       "0                          1  25.198986  ...                21.500873   \n",
+       "0                          1  26.764536  ...                20.928554   \n",
+       "0                          1  28.184169  ...                18.024346   \n",
+       "0                          1  29.961410  ...                19.832787   \n",
+       "0                          1  31.357056  ...                20.430319   \n",
+       "\n",
+       "              os_count         os  sb_count         sb     h        hw  \\\n",
+       "street_index                                                             \n",
+       "0                    2  50.000000         0        NaN   NaN       NaN   \n",
+       "0                    2  50.000000         0        NaN   NaN       NaN   \n",
+       "0                    2  38.796723         1  27.593446  24.0  0.869772   \n",
+       "0                    2  38.114846         1  26.229691  24.0  0.914994   \n",
+       "0                    2  37.706231         1  25.412462  24.0  0.944419   \n",
+       "0                    2  24.588764         1  25.198986  24.0  0.952419   \n",
+       "0                    2  20.742455         1  26.764536  24.0  0.896709   \n",
+       "0                    2  21.444652         1  28.184169  24.0  0.851542   \n",
+       "0                    2  22.325652         1  29.961410  24.0  0.801030   \n",
+       "0                    2  23.015854         1  31.357056  24.0  0.765378   \n",
+       "\n",
+       "                     bc  plot_seq_sb  plot_seq_sb_depth  \n",
+       "street_index                                             \n",
+       "0              0.000000    14.568200          19.240118  \n",
+       "0              0.000000    10.090066          18.011884  \n",
+       "0              2.491801    10.100149          15.658901  \n",
+       "0              5.604209     9.996735          27.216748  \n",
+       "0              6.825341     9.996961          32.570991  \n",
+       "0             11.811294     9.997186          29.171224  \n",
+       "0             16.722367     9.997412          25.070210  \n",
+       "0             15.669026     9.997637          21.235336  \n",
+       "0             13.593521     9.997863          24.073478  \n",
+       "0             13.355549     9.998088          25.161555  \n",
+       "\n",
+       "[10 rows x 30 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "point_df = sc.point_level()\n",
+    "point_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Again, it is a GeoDataFrame, this time with point geometry."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x1200 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = point_df.plot(\"os\", figsize=(12, 12), legend=True, markersize=5)\n",
+    "buildings.plot(ax=ax)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/momepy/__init__.py b/momepy/__init__.py
index b6b958b8..bf8e6168 100644
--- a/momepy/__init__.py
+++ b/momepy/__init__.py
@@ -17,6 +17,7 @@
 from .intensity import *
 from .preprocessing import *
 from .shape import *
+from .streetscape import *
 from .utils import *
 from .weights import *
 
diff --git a/momepy/datasets/bubenec.gpkg b/momepy/datasets/bubenec.gpkg
index b6db7605..420ae5f1 100644
Binary files a/momepy/datasets/bubenec.gpkg and b/momepy/datasets/bubenec.gpkg differ
diff --git a/momepy/streetscape.py b/momepy/streetscape.py
new file mode 100644
index 00000000..c4d18528
--- /dev/null
+++ b/momepy/streetscape.py
@@ -0,0 +1,2060 @@
+import math
+import warnings
+
+import geopandas as gpd
+import numpy as np
+import pandas as pd
+import shapely
+from shapely import LineString, MultiLineString, MultiPoint, Point
+
+import momepy
+
+
+class Streetscape:
+    def __init__(
+        self,
+        streets: gpd.GeoDataFrame,
+        buildings: gpd.GeoDataFrame,
+        sightline_length: float = 50,
+        tangent_length: float = 300,
+        sightline_spacing: float = 3,
+        intersection_offset: float = 0.5,
+        angle_tolerance: float = 5,
+        height_col: str | None = None,
+        category_col: str | None = None,
+    ) -> None:
+        """Streetscape analysis based on sightlines
+
+        This is a direct implementation of the algorithm proposed in Araldi and Fusco
+        2024.
+
+        Parameters
+        ----------
+        streets : gpd.GeoDataFrame
+            GeoDataFrame containing LineString geometry representing streets
+        buildings : gpd.GeoDataFrame
+            GeoDataFrame containing Polygon geometry representing buildings
+        sightline_length : float, optional
+            length of the sightline generated at each sightline point perpendiculary to
+            the street geometry, by default 50
+        tangent_length : float, optional
+            length of the sightline generated at each sightline point tangentially to
+            the street geometry, by default 300
+        sightline_spacing : float, optional
+            approximate distance between sightline points generated along streets,
+            by default 3
+        intersection_offset : float, optional
+            Offset to use at the beginning and the end of each LineString. The first
+            sightline point is generated at this distance from the start and the last
+            one is generated at this distance from the end of each geometry,
+            by default 0.5
+        angle_tolerance : float, optional
+            Maximum angle between sightlines that does not require infill lines to be
+            generated, by default 5
+        height_col : str, optional
+            name of a column of the buildings DataFrame containing the information
+            about the building height in meters.
+        category_col : str, optional
+            name of a column of the buildings DataFrame containing the information
+            about the building category encoded as integer labels.
+        """
+        self.sightline_length = sightline_length
+        self.tangent_length = tangent_length
+        self.sightline_spacing = sightline_spacing
+        self.intersection_offset = intersection_offset
+        self.angle_tolerance = angle_tolerance
+        self.height_col = height_col
+        self.category_col = category_col
+        self.building_categories_count = (
+            buildings[category_col].nunique() if category_col else 0
+        )
+
+        self.SIGHTLINE_LEFT = 0
+        self.SIGHTLINE_RIGHT = 1
+        self.SIGHTLINE_FRONT = 2
+        self.SIGHTLINE_BACK = 3
+
+        self.sightline_length_PER_SIGHT_TYPE = [
+            sightline_length,
+            sightline_length,
+            tangent_length,
+            tangent_length,
+        ]
+
+        streets = streets.copy()
+        streets.geometry = streets.force_2d()
+
+        nodes, edges = momepy.nx_to_gdf(
+            momepy.node_degree(momepy.gdf_to_nx(streets, preserve_index=True))
+        )
+        edges["n1_degree"] = nodes.degree.loc[edges.node_start].values
+        edges["n2_degree"] = nodes.degree.loc[edges.node_end].values
+        edges["dead_end_left"] = edges["n1_degree"] == 1
+        edges["dead_end_right"] = edges["n2_degree"] == 1
+        edges["street_index"] = edges.index
+
+        self.streets = edges
+
+        buildings = buildings.copy()
+        buildings["street_index"] = np.arange(len(buildings))
+        self.buildings = buildings
+
+        self.rtree_buildings = self.buildings.sindex
+
+        self._compute_sightline_indicators_full()
+
+    # return empty list if no sight line could be build du to total road length
+    def _compute_sightlines(
+        self,
+        line: LineString,
+        dead_end_start,
+        dead_end_end,
+    ):
+        # FIRST PART : PERPENDICULAR SIGHTLINES #
+
+        # Calculate the number of profiles to generate
+        line_length = line.length
+
+        remaining_length = line_length - 2 * self.intersection_offset
+        if remaining_length < self.sightline_spacing:
+            # no sight line
+            return (
+                gpd.GeoDataFrame(columns=["geometry", "point_id", "sight_type"]),
+                [],
+                [],
+            )
+
+        distances = [self.intersection_offset]
+        nb_inter_nodes = int(math.floor(remaining_length / self.sightline_spacing))
+        offset = remaining_length / nb_inter_nodes
+        distance = self.intersection_offset
+
+        for _ in range(0, nb_inter_nodes):
+            distance = distance + offset
+            distances.append(distance)
+
+        results_sight_points = []
+        results_sight_points_distances = []
+        results_sightlines = []
+
+        previous_sigh_line_left = None
+        previous_sigh_line_right = None
+
+        # semi_ortho_segment_size = self.sightline_spacing/2
+        semi_ortho_segment_size = self.intersection_offset / 2
+
+        sightline_index = 0
+
+        last_pure_sightline_left_position_in_array = -1
+
+        field_geometry = 0
+        field_uid = 1
+
+        # SECOND PART : TANGENT SIGHTLINES #
+
+        # Start iterating along the line
+        for distance in distances:
+            # Get the start, mid and end points for this segment
+
+            seg_st = line.interpolate(distance - semi_ortho_segment_size)
+            seg_mid = line.interpolate(distance)
+            seg_end = line.interpolate(distance + semi_ortho_segment_size)
+
+            # Get a displacement vector for this segment
+            vec = np.array(
+                [
+                    [
+                        seg_end.x - seg_st.x,
+                    ],
+                    [
+                        seg_end.y - seg_st.y,
+                    ],
+                ]
+            )
+
+            # Rotate the vector 90 deg clockwise and 90 deg counter clockwise
+            rot_anti = np.array([[0, -1], [1, 0]])
+            rot_clock = np.array([[0, 1], [-1, 0]])
+            vec_anti = np.dot(rot_anti, vec)
+            vec_clock = np.dot(rot_clock, vec)
+
+            # Normalise the perpendicular vectors
+            len_anti = ((vec_anti**2).sum()) ** 0.5
+            vec_anti = vec_anti / len_anti
+            len_clock = ((vec_clock**2).sum()) ** 0.5
+            vec_clock = vec_clock / len_clock
+
+            # Scale them up to the profile length
+            vec_anti = vec_anti * self.sightline_length
+            vec_clock = vec_clock * self.sightline_length
+
+            # Calculate displacements from midpoint
+            prof_st = (seg_mid.x + float(vec_anti[0]), seg_mid.y + float(vec_anti[1]))
+            prof_end = (
+                seg_mid.x + float(vec_clock[0]),
+                seg_mid.y + float(vec_clock[1]),
+            )
+
+            results_sight_points.append(seg_mid)
+            results_sight_points_distances.append(distance)
+
+            sightline_left = LineString([seg_mid, prof_st])
+            sightline_right = LineString([seg_mid, prof_end])
+
+            # append LEFT sight line
+            rec = [
+                sightline_left,  # field_geometry
+                sightline_index,  # field_uid
+                self.SIGHTLINE_LEFT,  # FIELD_type
+            ]
+            results_sightlines.append(rec)
+
+            # back up for dead end population
+            last_pure_sightline_left_position_in_array = len(results_sightlines) - 1
+
+            # append RIGHT sight line
+            rec = [
+                sightline_right,  # field_geometry
+                sightline_index,  # field_uid
+                self.SIGHTLINE_RIGHT,  # FIELD_type
+            ]
+            results_sightlines.append(rec)
+
+            line_tan_back = LineString(
+                [
+                    seg_mid,
+                    rotate(prof_end[0], prof_end[1], seg_mid.x, seg_mid.y, rad_90),
+                ]
+            )
+            line_tan_front = LineString(
+                [seg_mid, rotate(prof_st[0], prof_st[1], seg_mid.x, seg_mid.y, rad_90)]
+            )
+
+            # extends tanline to reach parametrized width
+            line_tan_back = extend_line_end(line_tan_back, self.tangent_length)
+            line_tan_front = extend_line_end(line_tan_front, self.tangent_length)
+
+            # append tangent sigline front view
+            rec = [
+                line_tan_back,  # field_geometry
+                sightline_index,  # FIELD_type
+                self.SIGHTLINE_BACK,
+            ]
+            results_sightlines.append(rec)
+
+            # append tangent sigline front view
+            rec = [
+                line_tan_front,  # field_geometry
+                sightline_index,  # field_uid
+                self.SIGHTLINE_FRONT,
+            ]
+            results_sightlines.append(rec)
+
+            # THIRD PART: SIGHTLINE ENRICHMENT #
+
+            # Populate lost space between consecutive sight lines with high deviation
+            # (>angle_tolerance)
+            if previous_sigh_line_left is not None:
+                for this_line, prev_line, side in [
+                    (sightline_left, previous_sigh_line_left, self.SIGHTLINE_LEFT),
+                    (sightline_right, previous_sigh_line_right, self.SIGHTLINE_RIGHT),
+                ]:
+                    # angle between consecutive sight line
+                    deviation = round(lines_angle(prev_line, this_line), 1)
+                    # DEBUG_VALUES.append([this_line.coords[1],deviation])
+                    # condition 1: large deviation
+                    if abs(deviation) <= self.angle_tolerance:
+                        continue
+                    # condition 1: consecutive sight lines do not intersect
+
+                    if this_line.intersects(prev_line):
+                        continue
+
+                    nb_new_sightlines = int(
+                        math.floor(abs(deviation) / self.angle_tolerance)
+                    )
+                    nb_new_sightlines_this = nb_new_sightlines // 2
+                    nb_new_sightlines_prev = nb_new_sightlines - nb_new_sightlines_this
+                    delta_angle = deviation / (nb_new_sightlines)
+                    theta_rad = np.deg2rad(delta_angle)
+
+                    # add S2 new sight line on previous one
+                    angle = 0
+                    for _ in range(0, nb_new_sightlines_this):
+                        angle -= theta_rad
+                        x0 = this_line.coords[0][0]
+                        y0 = this_line.coords[0][1]
+                        x = this_line.coords[1][0]
+                        y = this_line.coords[1][1]
+                        new_line = LineString(
+                            [this_line.coords[0], rotate(x, y, x0, y0, angle)]
+                        )
+                        rec = [
+                            new_line,  # field_geometry
+                            sightline_index,  # field_uid
+                            side,  # FIELD_type
+                        ]
+                        results_sightlines.append(rec)
+
+                        # add S2 new sight line on this current sight line
+                    angle = 0
+                    for _ in range(0, nb_new_sightlines_prev):
+                        angle += theta_rad
+                        x0 = prev_line.coords[0][0]
+                        y0 = prev_line.coords[0][1]
+                        x = prev_line.coords[1][0]
+                        y = prev_line.coords[1][1]
+                        new_line = LineString(
+                            [prev_line.coords[0], rotate(x, y, x0, y0, angle)]
+                        )
+                        rec = [
+                            new_line,  # field_geometry
+                            sightline_index - 1,  # field_uid
+                            side,  # FIELD_type
+                        ]
+                        results_sightlines.append(rec)
+
+            # ==
+
+            # iterate
+            previous_sigh_line_left = sightline_left
+            previous_sigh_line_right = sightline_right
+
+            sightline_index += 1
+
+        # ==
+        # SPECIFIC ENRICHMENT FOR SIGHTPOINTS corresponding to DEAD ENDs
+        # ==
+        if dead_end_start or dead_end_end:
+            for prev_sg, this_sg, dead_end in [
+                (
+                    results_sightlines[0],
+                    results_sightlines[1],
+                    dead_end_start,
+                ),
+                (
+                    results_sightlines[last_pure_sightline_left_position_in_array + 1],
+                    results_sightlines[last_pure_sightline_left_position_in_array],
+                    dead_end_end,
+                ),
+            ]:
+                if not dead_end:
+                    continue
+                # angle between consecutive dead end sight line LEFT and RIGHT (~180)
+                prev_line = prev_sg[field_geometry]  # FIRST sight line LEFT side
+                this_line = this_sg[field_geometry]  # FIRST sight line LEFT side
+
+                # special case --> dead end .. so 180 °
+                deviation = 180
+
+                nb_new_sightlines = int(
+                    math.floor(abs(deviation) / self.angle_tolerance)
+                )
+                nb_new_sightlines_this = nb_new_sightlines // 2
+                nb_new_sightlines_prev = nb_new_sightlines - nb_new_sightlines_this
+                delta_angle = deviation / (nb_new_sightlines)
+                theta_rad = np.deg2rad(delta_angle)
+
+                # add S2 new sight line on previous one
+                angle = 0
+                for _ in range(0, nb_new_sightlines_this):
+                    angle -= theta_rad
+                    x0 = this_line.coords[0][0]
+                    y0 = this_line.coords[0][1]
+                    x = this_line.coords[1][0]
+                    y = this_line.coords[1][1]
+                    new_line = LineString(
+                        [this_line.coords[0], rotate(x, y, x0, y0, angle)]
+                    )
+
+                    rec = [
+                        new_line,  # field_geometry
+                        this_sg[field_uid],  # field_uid
+                        self.SIGHTLINE_LEFT,
+                    ]
+                    results_sightlines.append(rec)
+
+                    # add S2 new sight line on this current sight line
+                angle = 0
+                for _ in range(0, nb_new_sightlines_prev):
+                    angle += theta_rad
+                    x0 = prev_line.coords[0][0]
+                    y0 = prev_line.coords[0][1]
+                    x = prev_line.coords[1][0]
+                    y = prev_line.coords[1][1]
+                    new_line = LineString(
+                        [prev_line.coords[0], rotate(x, y, x0, y0, angle)]
+                    )
+                    rec = [
+                        new_line,  # field_geometry
+                        prev_sg[field_uid],  # field_uid
+                        self.SIGHTLINE_RIGHT,
+                    ]
+                    results_sightlines.append(rec)
+            # ==
+        return (
+            gpd.GeoDataFrame(
+                results_sightlines, columns=["geometry", "point_id", "sight_type"]
+            ),
+            results_sight_points,
+            results_sight_points_distances,
+        )
+
+    def _compute_sigthlines_indicators(self, street_row, optimize_on=True):
+        street_uid = street_row.street_index
+        street_geom = street_row.geometry
+
+        gdf_sightlines, sightlines_points, results_sight_points_distances = (
+            self._compute_sightlines(
+                street_geom, street_row.dead_end_left, street_row.dead_end_right
+            )
+        )
+
+        # per street sightpoints indicators
+        current_street_uid = street_uid
+        current_street_sightlines_points = sightlines_points
+        current_street_left_os_count = []
+        current_street_left_os = []
+        current_street_left_sb_count = []
+        current_street_left_sb = []
+        current_street_left_h = []
+        current_street_left_hw = []
+        current_street_right_os_count = []
+        current_street_right_os = []
+        current_street_right_sb_count = []
+        current_street_right_sb = []
+        current_street_right_h = []
+        current_street_right_hw = []
+
+        current_street_left_bc = []
+        current_street_right_bc = []
+
+        # SPARSE STORAGE (one value if set back is OK ever in intersightline)
+        current_street_left_seq_sb_ids = []
+        current_street_left_seq_sb_categories = []
+        current_street_right_seq_sb_ids = []
+        current_street_right_seq_sb_categories = []
+
+        current_street_front_sb = []
+        current_street_back_sb = []
+
+        # [Expanded] each time a sight line or intersight line occured
+        left_seq_sightlines_end_points = []
+        right_seq_sightlines_end_points = []
+
+        if sightlines_points is None:
+            current_street_sightlines_points = []
+            return [
+                current_street_uid,
+                current_street_sightlines_points,
+                current_street_left_os_count,
+                current_street_left_os,
+                current_street_left_sb_count,
+                current_street_left_sb,
+                current_street_left_h,
+                current_street_left_hw,
+                current_street_left_bc,
+                current_street_left_seq_sb_ids,
+                current_street_left_seq_sb_categories,
+                current_street_right_os_count,
+                current_street_right_os,
+                current_street_right_sb_count,
+                current_street_right_sb,
+                current_street_right_h,
+                current_street_right_hw,
+                current_street_right_bc,
+                current_street_right_seq_sb_ids,
+                current_street_right_seq_sb_categories,
+                current_street_front_sb,
+                current_street_back_sb,
+                left_seq_sightlines_end_points,
+                right_seq_sightlines_end_points,
+            ], None
+
+        # ------- SIGHT LINES
+        # Extract building in SIGHTLINES buffer (e.g: 50m)
+
+        # iterate throught sightlines groups.
+        # Eeach sigh points could have many sub sighpoint in case of snail effect)
+        for _, group in gdf_sightlines.groupby("point_id"):
+            front_sl_tan_sb = self.tangent_length
+            back_sl_tan_sb = self.tangent_length
+            left_sl_count = 0
+            left_sl_distance_total = 0
+            left_sl_building_count = 0
+            left_sl_building_sb_total = 0
+            left_sl_building_sb_height_total = 0
+            right_sl_count = 0
+            right_sl_distance_total = 0
+            right_sl_building_count = 0
+            right_sl_building_sb_total = 0
+            right_sl_building_sb_height_total = 0
+
+            left_sl_cr_total = 0
+            right_sl_cr_total = 0
+
+            # iterate throught each sightline links to the sigh point:
+            # LEFT(1-*),RIGHT(1-*),FRONT(1), BACK(1)
+            for row_s in group.itertuples(index=False):
+                sightline_geom = row_s.geometry
+                sightline_side = row_s.sight_type
+                sightline_length = self.sightline_length_PER_SIGHT_TYPE[sightline_side]
+                # extract possible candidates
+                if optimize_on and sightline_side >= self.SIGHTLINE_FRONT:
+                    # = OPTIM TEST
+                    # cut tan line in 3 block (~100m)
+                    length_3 = sightline_geom.length / 3.0
+                    a = sightline_geom.coords[0]
+                    b = sightline_geom.coords[-1]
+                    end_points = [
+                        sightline_geom.interpolate(length_3),
+                        sightline_geom.interpolate(length_3 * 2),
+                        b,
+                    ]
+
+                    gdf_sightline_buildings = None
+                    start_point = a
+                    for end_point in end_points:
+                        sub_line = LineString([start_point, end_point])
+                        gdf_sightline_buildings = self.buildings.iloc[
+                            self.rtree_buildings.query(sub_line, predicate="intersects")
+                        ]
+                        if len(gdf_sightline_buildings) > 0:
+                            break
+                        start_point = end_point
+                else:
+                    gdf_sightline_buildings = self.buildings.iloc[
+                        self.rtree_buildings.query(
+                            sightline_geom, predicate="intersects"
+                        )
+                    ]
+
+                s_pt1 = shapely.get_point(sightline_geom, 0)
+                endpoint = shapely.get_point(sightline_geom, -1)
+
+                # agregate
+                match_sl_distance = (
+                    sightline_length  # set max distance if no polygon intersect
+                )
+                match_sl_building_id = None
+                match_sl_building_category = None
+                match_sl_building_height = 0
+
+                sl_cr_total = 0
+                for _, res in gdf_sightline_buildings.iterrows():
+                    # building geom
+                    geom = res.geometry
+                    isect = sightline_geom.intersection(geom.exterior)
+                    if not isect.is_empty:
+                        dist = s_pt1.distance(isect)
+                        if dist < match_sl_distance:
+                            match_sl_distance = dist
+                            match_sl_building_id = res.street_index
+                            match_sl_building_height = (
+                                res[self.height_col] if self.height_col else np.nan
+                            )
+                            match_sl_building_category = (
+                                res[self.category_col] if self.category_col else None
+                            )
+
+                        # coverage ratio between sight line and candidate building
+                        # (geom: building geom)
+                        _coverage_isec = sightline_geom.intersection(geom)
+                        # display(type(coverage_isec))
+                        sl_cr_total += _coverage_isec.length
+
+                if sightline_side == self.SIGHTLINE_LEFT:
+                    left_sl_count += 1
+                    left_seq_sightlines_end_points.append(endpoint)
+                    left_sl_distance_total += match_sl_distance
+                    left_sl_cr_total += sl_cr_total
+                    if match_sl_building_id:
+                        left_sl_building_count += 1
+                        left_sl_building_sb_total += match_sl_distance
+                        left_sl_building_sb_height_total += match_sl_building_height
+                        # PREVALENCE: Emit each time a new setback or INTER-setback is
+                        # found (campact storage structure)
+                        current_street_left_seq_sb_ids.append(match_sl_building_id)
+                        current_street_left_seq_sb_categories.append(
+                            match_sl_building_category
+                        )
+
+                elif sightline_side == self.SIGHTLINE_RIGHT:
+                    right_sl_count += 1
+                    right_seq_sightlines_end_points.append(endpoint)
+                    right_sl_distance_total += match_sl_distance
+                    right_sl_cr_total += sl_cr_total
+                    if match_sl_building_id:
+                        right_sl_building_count += 1
+                        right_sl_building_sb_total += match_sl_distance
+                        right_sl_building_sb_height_total += match_sl_building_height
+                        # PREVALENCE: Emit each time a new setback or INTER-setback is
+                        # found (campact storage structure)
+                        current_street_right_seq_sb_ids.append(match_sl_building_id)
+                        current_street_right_seq_sb_categories.append(
+                            match_sl_building_category
+                        )
+
+                elif sightline_side == self.SIGHTLINE_BACK:
+                    back_sl_tan_sb = match_sl_distance
+                elif sightline_side == self.SIGHTLINE_FRONT:
+                    front_sl_tan_sb = match_sl_distance
+
+            # LEFT
+            left_os_count = left_sl_count
+            left_os = left_sl_distance_total / left_os_count
+            left_sb_count = left_sl_building_count
+            left_sb = np.nan
+            left_h = np.nan
+            left_hw = np.nan
+            if left_sb_count != 0:
+                left_sb = left_sl_building_sb_total / left_sb_count
+                left_h = left_sl_building_sb_height_total / left_sb_count
+                # HACk if sb = 0 --> 10cm
+                left_hw = left_h / max(left_sb, 0.1)
+            left_cr = left_sl_cr_total / left_os_count
+            # RIGHT
+            right_os_count = right_sl_count
+            right_os = right_sl_distance_total / right_os_count
+            right_sb_count = right_sl_building_count
+            right_sb = np.nan
+            right_h = np.nan
+            right_hw = np.nan
+            if right_sb_count != 0:
+                right_sb = right_sl_building_sb_total / right_sb_count
+                right_h = right_sl_building_sb_height_total / right_sb_count
+                # HACk if sb = 0 --> 10cm
+                right_hw = right_h / max(right_sb, 0.1)
+            right_cr = right_sl_cr_total / right_os_count
+
+            current_street_left_os_count.append(left_os_count)
+            current_street_left_os.append(left_os)
+            current_street_left_sb_count.append(left_sb_count)
+            current_street_left_sb.append(left_sb)
+            current_street_left_h.append(left_h)
+            current_street_left_hw.append(left_hw)
+            current_street_right_os_count.append(right_os_count)
+            current_street_right_os.append(right_os)
+            current_street_right_sb_count.append(right_sb_count)
+            current_street_right_sb.append(right_sb)
+            current_street_right_h.append(right_h)
+            current_street_right_hw.append(right_hw)
+            # FRONT / BACK
+            current_street_front_sb.append(front_sl_tan_sb)
+            current_street_back_sb.append(back_sl_tan_sb)
+            # COverage ratio Built up
+            current_street_left_bc.append(left_cr)
+            current_street_right_bc.append(right_cr)
+
+        return [
+            current_street_uid,
+            current_street_sightlines_points,
+            current_street_left_os_count,
+            current_street_left_os,
+            current_street_left_sb_count,
+            current_street_left_sb,
+            current_street_left_h,
+            current_street_left_hw,
+            current_street_left_bc,
+            current_street_left_seq_sb_ids,
+            current_street_left_seq_sb_categories,
+            current_street_right_os_count,
+            current_street_right_os,
+            current_street_right_sb_count,
+            current_street_right_sb,
+            current_street_right_h,
+            current_street_right_hw,
+            current_street_right_bc,
+            current_street_right_seq_sb_ids,
+            current_street_right_seq_sb_categories,
+            current_street_front_sb,
+            current_street_back_sb,
+            left_seq_sightlines_end_points,
+            right_seq_sightlines_end_points,
+        ], gdf_sightlines
+
+    def _compute_sightline_indicators_full(self):
+        values = []
+
+        for street_row in self.streets[
+            ["street_index", "geometry", "dead_end_left", "dead_end_right"]
+        ].itertuples(index=False):
+            indicators, _ = self._compute_sigthlines_indicators(street_row)
+            values.append(indicators)
+
+        df = pd.DataFrame(
+            values,
+            columns=[
+                "street_index",
+                "sightline_points",
+                "left_os_count",
+                "left_os",
+                "left_sb_count",
+                "left_sb",
+                "left_h",
+                "left_hw",
+                "left_bc",
+                "left_seq_sb_ids",
+                "left_seq_sb_categories",
+                "right_os_count",
+                "right_os",
+                "right_sb_count",
+                "right_sb",
+                "right_h",
+                "right_hw",
+                "right_bc",
+                "right_seq_sb_ids",
+                "right_seq_sb_categories",
+                "front_sb",
+                "back_sb",
+                "left_seq_os_endpoints",
+                "right_seq_os_endpoints",
+            ],
+        )
+        df = df.set_index("street_index")
+
+        df["nodes_degree_1"] = self.streets.apply(
+            lambda row: (
+                (1 if row.n1_degree == 1 else 0) + (1 if row.n2_degree == 1 else 0)
+            )
+            / 2,
+            axis=1,
+        )
+
+        df["nodes_degree_4"] = self.streets.apply(
+            lambda row: (
+                (1 if row.n1_degree == 4 else 0) + (1 if row.n2_degree == 4 else 0)
+            )
+            / 2,
+            axis=1,
+        )
+
+        df["nodes_degree_3_5_plus"] = self.streets.apply(
+            lambda row: (
+                (1 if row.n1_degree == 3 or row.n1_degree >= 5 else 0)
+                + (1 if row.n2_degree == 3 or row.n2_degree >= 5 else 0)
+            )
+            / 2,
+            axis=1,
+        )
+        df["street_length"] = self.streets.length
+        df["windingness"] = 1 - momepy.linearity(self.streets)
+
+        self._sightline_indicators = df
+
+    def _compute_sigthlines_plot_indicators_one_side(
+        self, sightline_points, os_count, seq_os_endpoint
+    ):
+        parcel_sb_count = []
+        parcel_seq_sb_ids = []
+        parcel_seq_sb = []
+        parcel_seq_sb_depth = []
+
+        n = len(sightline_points)
+        if n == 0:
+            parcel_sb_count = [0] * n
+            return [
+                parcel_sb_count,
+                parcel_seq_sb_ids,
+                parcel_seq_sb,
+                parcel_seq_sb_depth,
+            ]
+
+        idx_end_point = 0
+
+        for sight_point, os_count_ in zip(sightline_points, os_count, strict=False):
+            n_sightlines_touching = 0
+            for _ in range(os_count_):
+                sightline_geom = LineString(
+                    [sight_point, seq_os_endpoint[idx_end_point]]
+                )
+                s_pt1 = Point(sightline_geom.coords[0])
+
+                gdf_items = self.plots.iloc[
+                    self.rtree_parcels.query(sightline_geom, predicate="intersects")
+                ]
+
+                match_distance = (
+                    self.sightline_length  # set max distance if no polygon intersect
+                )
+                match_id = None
+                match_geom = None
+
+                if not gdf_items.empty:
+                    _distances = gdf_items.exterior.intersection(
+                        sightline_geom
+                    ).distance(s_pt1)
+                    match_id = _distances.idxmin()
+                    match_distance = _distances.min()
+                    match_geom = gdf_items.geometry[match_id]
+
+                # ---------------
+                # result in intersightline
+                if match_id is not None:
+                    n_sightlines_touching += 1
+                    parcel_seq_sb_ids.append(match_id)
+                    parcel_seq_sb.append(match_distance)
+                    # compute depth of plot intersect sighline etendue
+                    if not match_geom.is_valid:
+                        match_geom = match_geom.buffer(0)
+                    isec = match_geom.intersection(
+                        extend_line_end(
+                            sightline_geom, self.sightline_plot_depth_extension
+                        )
+                    )
+                    if (not isinstance(isec, LineString)) and (
+                        not isinstance(isec, MultiLineString)
+                    ):
+                        raise Exception("Not allowed: intersection is not of type Line")
+                    parcel_seq_sb_depth.append(isec.length)
+
+                # ------- iterate
+                idx_end_point += 1
+
+            parcel_sb_count.append(n_sightlines_touching)
+
+        return [parcel_sb_count, parcel_seq_sb_ids, parcel_seq_sb, parcel_seq_sb_depth]
+
+    def compute_plots(
+        self, plots: gpd.GeoDataFrame, sightline_plot_depth_extension: float = 300
+    ):
+        self.sightline_plot_depth_extension = sightline_plot_depth_extension
+
+        self.rtree_parcels = plots.sindex
+        plots = plots.copy()
+        plots["parcel_id"] = np.arange(len(plots))
+        self.plots = plots
+        self.plots["perimeter"] = self.plots.length
+
+        values = []
+
+        for uid, row in self._sightline_indicators.iterrows():
+            sightline_values = [uid]
+
+            side_values = self._compute_sigthlines_plot_indicators_one_side(
+                row.sightline_points, row.left_os_count, row.left_seq_os_endpoints
+            )
+            sightline_values += side_values
+
+            side_values = self._compute_sigthlines_plot_indicators_one_side(
+                row.sightline_points, row.right_os_count, row.right_seq_os_endpoints
+            )
+            sightline_values += side_values
+
+            values.append(sightline_values)
+
+        df = pd.DataFrame(
+            values,
+            columns=[
+                "street_index",
+                "left_parcel_sb_count",
+                "left_parcel_seq_sb_ids",
+                "left_parcel_seq_sb",
+                "left_parcel_seq_sb_depth",
+                "right_parcel_sb_count",
+                "right_parcel_seq_sb_ids",
+                "right_parcel_seq_sb",
+                "right_parcel_seq_sb_depth",
+            ],
+        )
+        df = df.set_index("street_index").join(self._sightline_indicators.street_length)
+
+        self._plot_indicators = df
+
+    def _aggregate_plots(self):
+        values = []
+
+        for street_uid, row in self._plot_indicators.iterrows():
+            left_parcel_sb_count = row.left_parcel_sb_count
+            left_parcel_seq_sb_ids = row.left_parcel_seq_sb_ids
+            left_parcel_seq_sb = row.left_parcel_seq_sb
+            left_parcel_seq_sb_depth = row.left_parcel_seq_sb_depth
+            right_parcel_sb_count = row.right_parcel_sb_count
+            right_parcel_seq_sb_ids = row.right_parcel_seq_sb_ids
+            right_parcel_seq_sb = row.right_parcel_seq_sb
+            right_parcel_seq_sb_depth = row.right_parcel_seq_sb_depth
+            street_length = row.street_length
+
+            n = len(left_parcel_sb_count)
+            if n == 0:
+                values.append(
+                    [
+                        street_uid,
+                        0,
+                        0,  # np_l, np_r
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                        np.nan,
+                    ]
+                )
+                continue
+
+            left_parcel_seq_sb_depth = [
+                d if d >= 1 else 1 for d in left_parcel_seq_sb_depth
+            ]
+            right_parcel_seq_sb_depth = [
+                d if d >= 1 else 1 for d in right_parcel_seq_sb_depth
+            ]
+
+            left_unique_ids = set(left_parcel_seq_sb_ids)
+            right_unique_ids = set(right_parcel_seq_sb_ids)
+            all_unique_ids = left_unique_ids.union(right_unique_ids)
+
+            left_parcel_freq = len(left_unique_ids) / street_length
+            right_parcel_freq = len(right_unique_ids) / street_length
+            parcel_freq = len(all_unique_ids) / street_length
+
+            # compute sightline weights
+            left_sight_weight = []
+            # iterate all sight point
+            for sb_count in left_parcel_sb_count:
+                if sb_count != 0:
+                    w = 1.0 / sb_count
+                    for _ in range(sb_count):
+                        left_sight_weight.append(w)
+
+            right_sight_weight = []
+            # iterate all sight point
+            for sb_count in right_parcel_sb_count:
+                if sb_count != 0:
+                    w = 1.0 / sb_count
+                    for _ in range(sb_count):
+                        right_sight_weight.append(w)
+
+            # build depth dataframe with interzsighline weight
+            df_depth = [
+                [parcel_id, w, sb, depth, self.SIGHTLINE_LEFT]
+                for parcel_id, w, sb, depth in zip(
+                    left_parcel_seq_sb_ids,
+                    left_sight_weight,
+                    left_parcel_seq_sb,
+                    left_parcel_seq_sb_depth,
+                    strict=False,
+                )
+            ]
+            df_depth += [
+                [parcel_id, w, sb, depth, self.SIGHTLINE_RIGHT]
+                for parcel_id, w, sb, depth in zip(
+                    right_parcel_seq_sb_ids,
+                    right_sight_weight,
+                    right_parcel_seq_sb,
+                    right_parcel_seq_sb_depth,
+                    strict=False,
+                )
+            ]
+
+            df_depth = pd.DataFrame(
+                df_depth, columns=["parcel_id", "w", "sb", "depth", "side"]
+            ).set_index("parcel_id")
+            df_depth["w_sb"] = df_depth.w * df_depth.sb
+            df_depth["w_depth"] = df_depth.w * df_depth.depth
+
+            df_depth_left = df_depth[df_depth.side == self.SIGHTLINE_LEFT]
+            df_depth_right = df_depth[df_depth.side == self.SIGHTLINE_RIGHT]
+
+            np_l = int(df_depth_left.w.sum())
+            np_r = int(df_depth_right.w.sum())
+            np_lr = np_l + np_r
+
+            left_parcel_sb = (
+                df_depth_left.w_sb.sum() / np_l if np_l > 0 else self.sightline_length
+            )
+            right_parcel_sb = (
+                df_depth_right.w_sb.sum() / np_r if np_r > 0 else self.sightline_length
+            )
+            parcel_sb = (
+                df_depth.w_sb.sum() / np_lr if np_lr > 0 else self.sightline_length
+            )
+
+            left_parcel_depth = df_depth_left.w_depth.sum() / np_l if np_l > 0 else 0
+            right_parcel_depth = df_depth_right.w_depth.sum() / np_r if np_r > 0 else 0
+            parcel_depth = df_depth.w_depth.sum() / np_lr if np_lr > 0 else 0
+
+            wd_ratio_list = []
+            wp_ratio_list = []
+            # TODO: this thing is pretty terrible and needs to be completely redone
+            # It is a massive bottleneck
+            for df in [df_depth, df_depth_left, df_depth_right]:
+                if len(df) == 0:
+                    wd_ratio_list.append(0)
+                    wp_ratio_list.append(0)
+                    continue
+
+                df = (
+                    df[["w", "w_depth"]]
+                    .groupby(level=0)
+                    .aggregate(
+                        nb=pd.NamedAgg(column="w", aggfunc=len),
+                        w_sum=pd.NamedAgg(column="w", aggfunc="sum"),
+                        w_depth=pd.NamedAgg(column="w_depth", aggfunc="mean"),
+                    )
+                )
+
+                df = df.join(self.plots.perimeter)
+                sum_nb = df.nb.sum()
+
+                wd_ratio = (
+                    (df.w_sum * self.sightline_spacing * df.nb) / df.w_depth
+                ).sum() / sum_nb
+                wp_ratio = (
+                    (df.w_sum * self.sightline_spacing * df.nb) / df.perimeter
+                ).sum() / sum_nb
+                wd_ratio_list.append(wd_ratio)
+                wp_ratio_list.append(wp_ratio)
+
+            values.append(
+                [
+                    street_uid,
+                    np_l,
+                    np_r,
+                    parcel_sb,
+                    left_parcel_sb,
+                    right_parcel_sb,
+                    parcel_freq,
+                    left_parcel_freq,
+                    right_parcel_freq,
+                    parcel_depth,
+                    left_parcel_depth,
+                    right_parcel_depth,
+                ]
+                + wd_ratio_list
+                + wp_ratio_list
+            )
+
+        columns = [
+            "uid",
+            "left_plot_count",
+            "right_plot_count",
+            "plot_sb",
+            "left_plot_sb",
+            "right_plot_sb",
+            "plot_freq",
+            "left_plot_freq",
+            "right_plot_freq",
+            "plot_depth",
+            "left_plot_depth",
+            "right_plot_depth",
+            "plot_WD_ratio",
+            "left_plot_WD_ratio",
+            "right_plot_WD_ratio",
+            "plot_WP_ratio",
+            "left_plot_WP_ratio",
+            "right_plot_WP_ratio",
+        ]
+
+        self._aggregate_plot_data = pd.DataFrame(values, columns=columns).set_index(
+            "uid"
+        )
+
+    def _compute_slope(self, road_row):
+        start = road_row.sl_start  # Point z
+        end = road_row.sl_end  # Point z
+        slp = road_row.sl_points  # Multipoint z
+
+        if slp is None:
+            # Case when there is no sight line point (e.g. when the road is too short)
+            # just computes slope between start and end
+            if start.z == self.NODATA_RASTER or end.z == self.NODATA_RASTER:
+                # Case when there is at least one invalid z coord
+                return 0, 0, 0, False
+            slope_percent = abs(start.z - end.z) / shapely.distance(start, end)
+            slope_degree = math.degrees(math.atan(slope_percent))
+
+            return slope_percent, slope_degree, 1, True
+
+        # From Multipoint z to Point z list
+        slp_list = list(slp.geoms)
+
+        points = []
+
+        points.append(start)
+        # From Point z list to all points list
+        for p in slp_list:
+            points.append(p)
+        points.append(end)
+
+        # number of points
+        nb_points = len([start]) + len([end]) + len(slp_list)
+
+        # temporary variables to store inter slope values
+        sum_slope_percent = 0
+        sum_slope_radian = 0
+        sum_nb_points = 0
+
+        # if there is one or more sight line points
+        for i in range(1, nb_points - 1):
+            a = points[i - 1]
+            b = points[i + 1]
+
+            if a.z == self.NODATA_RASTER or b.z == self.NODATA_RASTER:
+                # Case when there is no valid z coord in slpoint
+                continue
+
+            sum_nb_points += 1
+            inter_slope_percent = abs(a.z - b.z) / shapely.distance(a, b)
+
+            sum_slope_percent += inter_slope_percent
+            sum_slope_radian += math.atan(inter_slope_percent)
+
+        if sum_nb_points == 0:
+            # Case when no slpoint has a valid z coord
+            # Unable to compute slope
+            return 0, 0, 0, False
+
+        # compute mean of inter slopes
+        slope_percent = sum_slope_percent / sum_nb_points
+        slope_degree = math.degrees(sum_slope_radian / sum_nb_points)
+
+        return slope_degree, slope_percent, sum_nb_points, True
+
+    def compute_slope(self, raster):
+        import rioxarray  # noqa: F401
+        import xvec  # noqa: F401
+
+        self.NODATA_RASTER = raster.rio.nodata
+
+        start_points = shapely.get_point(self.streets.geometry, 0)
+        end_points = shapely.get_point(self.streets.geometry, -1)
+
+        # Extract z coords from raster
+        z_start = (
+            raster.drop_vars("spatial_ref")
+            .xvec.extract_points(points=start_points, x_coords="x", y_coords="y")
+            .xvec.to_geopandas()
+        )
+        z_start = z_start.rename(
+            columns={k: "z" for k in z_start.columns.drop("geometry")}
+        )
+
+        # Append z values to points
+        z_start["start_point_3d"] = shapely.points(
+            *shapely.get_coordinates(start_points.geometry).T, z=z_start["z"]
+        )
+
+        # Extract z coords from raster
+        z_end = (
+            raster.drop_vars("spatial_ref")
+            .xvec.extract_points(points=end_points, x_coords="x", y_coords="y")
+            .xvec.to_geopandas()
+        )
+        z_end = z_end.rename(columns={k: "z" for k in z_end.columns.drop("geometry")})
+
+        # Append z values to points
+        z_end["end_point_3d"] = shapely.points(
+            *shapely.get_coordinates(end_points.geometry).T, z=z_end["z"]
+        )
+
+        z_points_list = []
+
+        for row in self._sightline_indicators["sightline_points"].apply(
+            lambda x: MultiPoint(x) if x else None
+        ):
+            if row is not None:
+                points = row.geoms
+
+                z_points = (
+                    raster.drop_vars("spatial_ref")
+                    .xvec.extract_points(points=points, x_coords="x", y_coords="y")
+                    .xvec.to_geopandas()
+                )
+                z_points = z_points.rename(
+                    columns={k: "z" for k in z_points.columns.drop("geometry")}
+                )
+
+                z_points["geometry"] = shapely.points(
+                    *shapely.get_coordinates(z_points.geometry).T, z=z_points["z"]
+                )
+                z_points = z_points.drop(columns="z")
+
+                multipoint = MultiPoint(z_points["geometry"].tolist())
+
+            else:
+                multipoint = None
+
+            z_points_list.append(multipoint)
+
+        sightlines = pd.concat(
+            [z_start[["start_point_3d"]], z_end[["end_point_3d"]]], axis=1
+        )
+
+        sightlines = sightlines.rename(
+            columns={"start_point_3d": "sl_start", "end_point_3d": "sl_end"}
+        )
+
+        sightlines["sl_points"] = z_points_list
+
+        slope_values = []
+
+        for _, road_row in sightlines.iterrows():
+            slope_degree, slope_percent, n_slopes, slope_valid = self._compute_slope(
+                road_row
+            )
+
+            slope_values.append([slope_degree, slope_percent, n_slopes, slope_valid])
+
+        self.slope = pd.DataFrame(
+            slope_values,
+            columns=["slope_degree", "slope_percent", "n_slopes", "slope_valid"],
+        )
+
+    # 0.5 contribution if parralel with previous sightpoint setback
+    # 0.5 contribution if parralel with next sightpoint setback
+    def _compute_parallelism_factor(self, side_sb, side_sb_count, max_distance=999):
+        if side_sb_count is None or len(side_sb_count) == 0:
+            return []
+        is_parralel_with_next = []
+        for sb_a, sb_a_count, sb_b, sb_b_count in zip(
+            side_sb[0:-1],
+            side_sb_count[0:-1],
+            side_sb[1:],
+            side_sb_count[1:],
+            strict=False,
+        ):
+            if sb_a_count == 0 or sb_b_count == 0:
+                is_parralel_with_next.append(False)
+                continue
+            if max_distance is None or max(sb_a, sb_b) <= max_distance:
+                is_parralel_with_next.append(
+                    abs(sb_a - sb_b) < self.sightline_spacing / 3
+                )
+            else:
+                is_parralel_with_next.append(False)
+        # choice for last point
+        is_parralel_with_next.append(False)
+
+        result = []
+        prev_parralel = False
+        for next_parralel in is_parralel_with_next:
+            # Ajouter condition su
+            factor = 0
+            if prev_parralel:  # max_distance
+                # STOP
+                factor += 0.5
+            if next_parralel:
+                factor += 0.5
+            result.append(factor)
+            prev_parralel = next_parralel
+
+        return result
+
+    def _compute_parallelism_indicators(
+        self,
+        left_sb,
+        left_sb_count,
+        right_sb,
+        right_sb_count,
+        n,
+        n_l,
+        n_r,
+        max_distance=None,
+    ):
+        parallel_left_factors = self._compute_parallelism_factor(
+            left_sb, left_sb_count, max_distance
+        )
+        parallel_right_factors = self._compute_parallelism_factor(
+            right_sb, right_sb_count, max_distance
+        )
+
+        parallel_left_total = sum(parallel_left_factors)
+        parallel_right_total = sum(parallel_right_factors)
+
+        ind_left_par_tot = parallel_left_total / (n - 1) if n > 1 else np.nan
+        ind_left_par_rel = parallel_left_total / (n_l - 1) if n_l > 1 else np.nan
+
+        ind_right_par_tot = parallel_right_total / (n - 1) if n > 1 else np.nan
+        ind_right_par_rel = parallel_right_total / (n_r - 1) if n_r > 1 else np.nan
+
+        ind_par_tot = np.nan
+        if n > 1:
+            ind_par_tot = (parallel_left_total + parallel_right_total) / (2 * n - 2)
+
+        ind_par_rel = np.nan
+        if n_l > 1 or n_r > 1:
+            ind_par_rel = (parallel_left_total + parallel_right_total) / (
+                max(1, n_l) + max(1, n_r) - 2
+            )
+
+        return (
+            ind_left_par_tot,
+            ind_left_par_rel,
+            ind_right_par_tot,
+            ind_right_par_rel,
+            ind_par_tot,
+            ind_par_rel,
+        )
+
+    def street_level(self):
+        values = []
+
+        for street_uid, row in self._sightline_indicators.iterrows():
+            street_length = row.street_length
+
+            left_os_count = row.left_os_count
+            left_os = row.left_os
+            left_sb_count = row.left_sb_count
+            left_sb = row.left_sb
+            left_h = row.left_h
+            left_hw = row.left_hw
+            right_os = row.right_os
+            right_sb_count = row.right_sb_count
+            right_sb = row.right_sb
+            right_h = row.right_h
+            right_hw = row.right_hw
+
+            left_bc = row.left_bc
+            left_seq_sb_ids = row.left_seq_sb_ids
+
+            right_bc = row.right_bc
+            right_seq_sb_ids = row.right_seq_sb_ids
+
+            front_sb = row.front_sb
+            back_sb = row.back_sb
+
+            n = len(left_os_count)
+            if n == 0:
+                continue
+
+            # ------------------------
+            # OPENNESS
+            # ------------------------
+            sum_left_os = np.sum(left_os)
+            sum_right_os = np.sum(right_os)
+
+            ind_left_os = sum_left_os / n
+            ind_right_os = sum_right_os / n
+            ind_os = ind_left_os + ind_right_os  # ==(left_os+right_os)/n
+
+            full_os = [le + r for le, r in zip(left_os, right_os, strict=False)]
+            # mediane >> med
+            ind_left_os_med = np.median(left_os)
+            ind_right_os_med = np.median(right_os)
+            ind_os_med = np.median(full_os)
+
+            # OPENNESS ROUGHNESS
+            sum_square_error_left_os = np.sum(
+                [(os - ind_left_os) ** 2 for os in left_os]
+            )
+            sum_square_error_right_os = np.sum(
+                [(os - ind_right_os) ** 2 for os in right_os]
+            )
+            sum_abs_error_left_os = np.sum([abs(os - ind_left_os) for os in left_os])
+            sum_abs_error_right_os = np.sum([abs(os - ind_right_os) for os in right_os])
+            ind_os_std = math.sqrt(
+                (sum_square_error_left_os + sum_square_error_right_os) / (2 * n - 1)
+            )
+            ind_os_mad = (sum_abs_error_left_os + sum_abs_error_right_os) / (2 * n)
+
+            ind_left_os_std = 0  # default
+            ind_right_os_std = 0  # default
+            ind_left_os_mad = 0  # default
+            ind_right_os_mad = 0  # default
+
+            ind_left_os_mad = sum_abs_error_left_os / n
+            ind_right_os_mad = sum_abs_error_right_os / n
+            if n > 1:
+                ind_left_os_std = math.sqrt((sum_square_error_left_os) / (n - 1))
+                ind_right_os_std = math.sqrt((sum_square_error_right_os) / (n - 1))
+
+            sum_abs_error_left_os_med = np.sum(
+                [abs(os - ind_left_os_med) for os in left_os]
+            )
+            sum_abs_error_right_os_med = np.sum(
+                [abs(os - ind_right_os_med) for os in right_os]
+            )
+            ind_left_os_mad_med = sum_abs_error_left_os_med / n
+            ind_right_os_mad_med = sum_abs_error_right_os_med / n
+            ind_os_mad_med = (
+                sum_abs_error_left_os_med + sum_abs_error_right_os_med
+            ) / (2 * n)
+
+            # ------------------------
+            # SETBACK
+            # ------------------------
+            rel_left_sb = [x for x in left_sb if not math.isnan(x)]
+            rel_right_sb = [x for x in right_sb if not math.isnan(x)]
+            n_l = len(rel_left_sb)
+            n_r = len(rel_right_sb)
+            n_l_plus_r = n_l + n_r
+            sum_left_sb = np.sum(rel_left_sb)
+            sum_right_sb = np.sum(rel_right_sb)
+
+            # SETBACK default values
+            ind_left_sb = sum_left_sb / n_l if n_l > 0 else self.sightline_length
+            ind_right_sb = sum_right_sb / n_r if n_r > 0 else self.sightline_length
+            ind_sb = (
+                (sum_left_sb + sum_right_sb) / (n_l_plus_r)
+                if n_l_plus_r > 0
+                else self.sightline_length
+            )
+
+            sum_square_error_left_sb = np.sum(
+                [(x - ind_left_sb) ** 2 for x in rel_left_sb]
+            )
+            sum_square_error_right_sb = np.sum(
+                [(x - ind_right_sb) ** 2 for x in rel_right_sb]
+            )
+
+            ind_left_sb_std = (
+                math.sqrt(sum_square_error_left_sb / (n_l - 1)) if n_l > 1 else 0
+            )
+            ind_right_sb_std = (
+                math.sqrt(sum_square_error_right_sb / (n_r - 1)) if n_r > 1 else 0
+            )
+            ind_sb_std = (
+                math.sqrt(
+                    (sum_square_error_left_sb + sum_square_error_right_sb)
+                    / (n_l_plus_r - 1)
+                )
+                if n_l_plus_r > 1
+                else 0
+            )
+
+            # medianes
+            ind_left_sb_med = (
+                np.median(rel_left_sb) if n_l > 0 else self.sightline_length
+            )
+            ind_right_sb_med = (
+                np.median(rel_right_sb) if n_r > 0 else self.sightline_length
+            )
+            ind_sb_med = (
+                np.median(np.concatenate([rel_left_sb, rel_right_sb]))
+                if n_l_plus_r > 0
+                else self.sightline_length
+            )
+
+            # mad
+            sum_abs_error_left_sb = np.sum([abs(x - ind_left_sb) for x in rel_left_sb])
+            sum_abs_error_right_sb = np.sum(
+                [abs(x - ind_right_sb) for x in rel_right_sb]
+            )
+            ind_left_sb_mad = sum_abs_error_left_sb / n_l if n_l > 0 else 0
+            ind_right_sb_mad = sum_abs_error_right_sb / n_r if n_r > 0 else 0
+            ind_sb_mad = (
+                (sum_abs_error_left_sb + sum_abs_error_right_sb) / (n_l_plus_r)
+                if n_l_plus_r > 0
+                else 0
+            )
+
+            # mad_med
+            sum_abs_error_left_sb_med = np.sum(
+                [abs(x - ind_left_sb_med) for x in rel_left_sb]
+            )
+            sum_abs_error_right_sb_med = np.sum(
+                [abs(x - ind_right_sb_med) for x in rel_right_sb]
+            )
+            ind_left_sb_mad_med = sum_abs_error_left_sb_med / n_l if n_l > 0 else 0
+            ind_right_sb_mad_med = sum_abs_error_right_sb_med / n_r if n_r > 0 else 0
+            ind_sb_mad_med = (
+                (sum_abs_error_left_sb_med + sum_abs_error_right_sb_med) / (n_l_plus_r)
+                if n_l_plus_r > 0
+                else 0
+            )
+
+            # ------------------------
+            # HEIGHT
+            # ------------------------
+            rel_left_h = [x for x in left_h if not math.isnan(x)]
+            rel_right_h = [x for x in right_h if not math.isnan(x)]
+            sum_left_h = np.sum(rel_left_h)
+            sum_right_h = np.sum(rel_right_h)
+
+            # HEIGHT AVERAGE default values
+            ind_left_h = sum_left_h / n_l if n_l > 0 else 0
+            ind_right_h = sum_right_h / n_r if n_r > 0 else 0
+            ind_h = (sum_left_h + sum_right_h) / (n_l_plus_r) if n_l_plus_r > 0 else 0
+
+            sum_square_error_left_h = np.sum(
+                [(x - ind_left_h) ** 2 for x in rel_left_h]
+            )
+            sum_square_error_right_h = np.sum(
+                [(x - ind_right_h) ** 2 for x in rel_right_h]
+            )
+
+            ind_left_h_std = (
+                math.sqrt(sum_square_error_left_h / (n_l - 1)) if n_l > 1 else 0
+            )
+            ind_right_h_std = (
+                math.sqrt(sum_square_error_right_h / (n_r - 1)) if n_r > 1 else 0
+            )
+            ind_h_std = (
+                math.sqrt(
+                    (sum_square_error_left_h + sum_square_error_right_h)
+                    / (n_l_plus_r - 1)
+                )
+                if n_l_plus_r > 1
+                else 0
+            )
+
+            # ------------------------
+            # CRosS_SECTION_PROPORTIOn (cross sectionnal ratio)
+            # ------------------------
+            rel_left_hw = [x for x in left_hw if not math.isnan(x)]
+            rel_right_hw = [x for x in right_hw if not math.isnan(x)]
+            sum_left_hw = np.sum(rel_left_hw)
+            sum_right_hw = np.sum(rel_right_hw)
+
+            ind_left_hw = sum_left_hw / n_l if n_l > 0 else 0
+            ind_right_hw = sum_right_hw / n_r if n_r > 0 else 0
+            ind_hw = (
+                (sum_left_hw + sum_right_hw) / (n_l_plus_r) if n_l_plus_r > 0 else 0
+            )
+
+            sum_square_error_left_hw = np.sum(
+                [(x - ind_left_hw) ** 2 for x in rel_left_hw]
+            )
+            sum_square_error_right_hw = np.sum(
+                [(x - ind_right_hw) ** 2 for x in rel_right_hw]
+            )
+
+            ind_left_hw_std = (
+                math.sqrt(sum_square_error_left_hw / (n_l - 1)) if n_l > 1 else 0
+            )
+            ind_right_hw_std = (
+                math.sqrt(sum_square_error_right_hw / (n_r - 1)) if n_r > 1 else 0
+            )
+            ind_hw_std = (
+                math.sqrt(
+                    (sum_square_error_left_hw + sum_square_error_right_hw)
+                    / (n_l_plus_r - 1)
+                )
+                if n_l_plus_r > 1
+                else 0
+            )
+
+            # --------------------------------
+            # CRosS_SECTIONNAL OPEn VIEW ANGLE
+            # --------------------------------
+            left_angles = [
+                np.rad2deg(np.arctan(hw)) if not math.isnan(hw) else 0 for hw in left_hw
+            ]
+            right_angles = [
+                np.rad2deg(np.arctan(hw)) if not math.isnan(hw) else 0
+                for hw in right_hw
+            ]
+
+            angles = [
+                180 - gamma_l - gamma_r
+                for gamma_l, gamma_r in zip(left_angles, right_angles, strict=False)
+            ]
+            ind_csosva = sum(angles) / n
+
+            # ------------------------
+            # TANGENTE Ratio (front+back/os if setback exists)
+            # ------------------------
+            all_tan = []
+            all_tan_ratio = []
+            for f, b, lf, r in zip(front_sb, back_sb, left_os, right_os, strict=False):
+                tan_value = f + b
+                all_tan.append(tan_value)
+                if not math.isnan(lf) and not math.isnan(r):
+                    all_tan_ratio.append(tan_value / (lf + r))
+
+            # Tan
+            ind_tan = np.sum(all_tan) / n
+            ind_tan_std = 0
+            if n > 1:
+                ind_tan_std = math.sqrt(
+                    np.sum([(x - ind_tan) ** 2 for x in all_tan]) / (n - 1)
+                )
+
+            # Tan ratio
+            ind_tan_ratio = 0
+            ind_tan_ratio_std = 0
+            n_tan_ratio = len(all_tan_ratio)
+            if n_tan_ratio > 0:
+                ind_tan_ratio = np.sum(all_tan_ratio) / n_tan_ratio
+                if n_tan_ratio > 1:
+                    ind_tan_ratio_std = math.sqrt(
+                        np.sum([(x - ind_tan_ratio) ** 2 for x in all_tan_ratio])
+                        / (n_tan_ratio - 1)
+                    )
+
+            # version de l'indictaur sans horizon (max = sightline_length)
+            (
+                ind_left_par_tot,
+                ind_left_par_rel,
+                ind_right_par_tot,
+                ind_right_par_rel,
+                ind_par_tot,
+                ind_par_rel,
+            ) = self._compute_parallelism_indicators(
+                left_sb,
+                left_sb_count,
+                right_sb,
+                right_sb_count,
+                n,
+                n_l,
+                n_r,
+                max_distance=None,
+            )
+
+            # version de l'indictaur a 15 mètres maximum
+            (
+                ind_left_par_tot_15,
+                ind_left_par_rel_15,
+                ind_right_par_tot_15,
+                ind_right_par_rel_15,
+                ind_par_tot_15,
+                ind_par_rel_15,
+            ) = self._compute_parallelism_indicators(
+                left_sb,
+                left_sb_count,
+                right_sb,
+                right_sb_count,
+                n,
+                n_l,
+                n_r,
+                max_distance=15,
+            )
+
+            # Built frequency
+            ind_left_built_freq = len(set(left_seq_sb_ids)) / street_length
+            ind_right_built_freq = len(set(right_seq_sb_ids)) / street_length
+            ind_built_freq = (
+                len(set(left_seq_sb_ids + right_seq_sb_ids)) / street_length
+            )
+
+            # Built coverage
+            ind_left_built_coverage = np.mean(left_bc) / self.sightline_length
+            ind_right_built_coverage = np.mean(right_bc) / self.sightline_length
+            ind_built_coverage = (
+                ind_left_built_coverage + ind_right_built_coverage
+            ) / 2
+
+            # Built category prevvvalence
+
+            values.append(
+                [
+                    street_uid,
+                    n,
+                    n_l,
+                    n_r,
+                    ind_left_os,
+                    ind_right_os,
+                    ind_os,
+                    ind_left_os_std,
+                    ind_right_os_std,
+                    ind_os_std,
+                    ind_left_os_mad,
+                    ind_right_os_mad,
+                    ind_os_mad,
+                    ind_left_os_med,
+                    ind_right_os_med,
+                    ind_os_med,
+                    ind_left_os_mad_med,
+                    ind_right_os_mad_med,
+                    ind_os_mad_med,
+                    ind_left_sb,
+                    ind_right_sb,
+                    ind_sb,
+                    ind_left_sb_std,
+                    ind_right_sb_std,
+                    ind_sb_std,
+                    ind_left_sb_mad,
+                    ind_right_sb_mad,
+                    ind_sb_mad,
+                    ind_left_sb_med,
+                    ind_right_sb_med,
+                    ind_sb_med,
+                    ind_left_sb_mad_med,
+                    ind_right_sb_mad_med,
+                    ind_sb_mad_med,
+                    ind_left_h,
+                    ind_right_h,
+                    ind_h,
+                    ind_left_h_std,
+                    ind_right_h_std,
+                    ind_h_std,
+                    ind_left_hw,
+                    ind_right_hw,
+                    ind_hw,
+                    ind_left_hw_std,
+                    ind_right_hw_std,
+                    ind_hw_std,
+                    ind_csosva,
+                    ind_tan,
+                    ind_tan_std,
+                    n_tan_ratio,
+                    ind_tan_ratio,
+                    ind_tan_ratio_std,
+                    ind_par_tot,
+                    ind_par_rel,
+                    ind_left_par_tot,
+                    ind_right_par_tot,
+                    ind_left_par_rel,
+                    ind_right_par_rel,
+                    ind_par_tot_15,
+                    ind_par_rel_15,
+                    ind_left_par_tot_15,
+                    ind_right_par_tot_15,
+                    ind_left_par_rel_15,
+                    ind_right_par_rel_15,
+                    ind_left_built_freq,
+                    ind_right_built_freq,
+                    ind_built_freq,
+                    ind_left_built_coverage,
+                    ind_right_built_coverage,
+                    ind_built_coverage,
+                ]
+            )
+
+        df = (
+            pd.DataFrame(
+                values,
+                columns=[
+                    "street_index",
+                    "N",
+                    "n_l",
+                    "n_r",
+                    "left_os",
+                    "right_os",
+                    "os",
+                    "left_os_std",
+                    "right_os_std",
+                    "os_std",
+                    "left_os_mad",
+                    "right_os_mad",
+                    "os_mad",
+                    "left_os_med",
+                    "right_os_med",
+                    "os_med",
+                    "left_os_mad_med",
+                    "right_os_mad_med",
+                    "os_mad_med",
+                    "left_sb",
+                    "right_sb",
+                    "sb",
+                    "left_sb_std",
+                    "right_sb_std",
+                    "sb_std",
+                    "left_sb_mad",
+                    "right_sb_mad",
+                    "sb_mad",
+                    "left_sb_med",
+                    "right_sb_med",
+                    "sb_med",
+                    "left_sb_mad_med",
+                    "right_sb_mad_med",
+                    "sb_mad_med",
+                    "left_h",
+                    "right_h",
+                    "H",
+                    "left_h_std",
+                    "right_h_std",
+                    "H_std",
+                    "left_hw",
+                    "right_hw",
+                    "HW",
+                    "left_hw_std",
+                    "right_hw_std",
+                    "HW_std",
+                    "csosva",
+                    "tan",
+                    "tan_std",
+                    "n_tan_ratio",
+                    "tan_ratio",
+                    "tan_ratio_std",
+                    "par_tot",
+                    "par_rel",
+                    "left_par_tot",
+                    "right_par_tot",
+                    "left_par_rel",
+                    "right_par_rel",
+                    "par_tot_15",
+                    "par_rel_15",
+                    "left_par_tot_15",
+                    "right_par_tot_15",
+                    "left_par_rel_15",
+                    "right_par_rel_15",
+                    "left_built_freq",
+                    "right_built_freq",
+                    "built_freq",
+                    "left_built_coverage",
+                    "right_built_coverage",
+                    "built_coverage",
+                ],
+            )
+            .set_index("street_index")
+            .join(
+                self._sightline_indicators[
+                    [
+                        "nodes_degree_1",
+                        "nodes_degree_4",
+                        "nodes_degree_3_5_plus",
+                        "street_length",
+                        "windingness",
+                    ]
+                ]
+            )
+        )
+
+        if self.category_col:
+            self._compute_prevalences()
+            df = df.join(self.prevalences)
+
+        if hasattr(self, "plots"):
+            self._aggregate_plots()
+            df = df.join(self._aggregate_plot_data)
+
+        if hasattr(self, "slope"):
+            df = df.join(self.slope)
+
+        return df.set_geometry(self.streets.geometry)
+
+    def _compute_building_category_prevalence_indicators(
+        self, sb_count, seq_sb_categories
+    ):
+        sb_sequence_id = 0
+        category_total_weight = 0
+        category_counters = np.zeros(self.building_categories_count)
+        for sb_count_ in sb_count:
+            if sb_count_ == 0:
+                continue
+            # add sight line contribution relative to snail effect
+            sb_weight = 1 / sb_count_
+            category_total_weight += 1
+            for _ in range(sb_count_):
+                category_counters[seq_sb_categories[sb_sequence_id]] += sb_weight
+                sb_sequence_id += 1
+
+        return category_counters, category_total_weight
+
+    def _compute_prevalences(self):
+        values = []
+
+        for street_uid, row in self._sightline_indicators.iterrows():
+            left_seq_sb_categories = row.left_seq_sb_categories
+            left_sb_count = row.left_sb_count
+            right_seq_sb_categories = row.right_seq_sb_categories
+            right_sb_count = row.right_sb_count
+
+            # left right totalizer
+            left_category_indicators, left_category_total_weight = (
+                self._compute_building_category_prevalence_indicators(
+                    left_sb_count, left_seq_sb_categories
+                )
+            )
+            right_category_indicators, right_category_total_weight = (
+                self._compute_building_category_prevalence_indicators(
+                    right_sb_count, right_seq_sb_categories
+                )
+            )
+
+            # global  totalizer
+            category_indicators = (
+                left_category_indicators + right_category_indicators
+            )  # numpy #add X+Y = Z wxhere zi=xi+yi
+            category_total_weight = (
+                left_category_total_weight + right_category_total_weight
+            )
+
+            left_category_indicators = (
+                left_category_indicators / left_category_total_weight
+                if left_category_total_weight != 0
+                else left_category_indicators
+            )
+            right_category_indicators = (
+                right_category_indicators / right_category_total_weight
+                if right_category_total_weight != 0
+                else right_category_indicators
+            )
+            category_indicators = (
+                category_indicators / category_total_weight
+                if category_total_weight != 0
+                else category_indicators
+            )
+
+            values.append([street_uid] + list(category_indicators))
+
+        columns = ["street_index"] + [
+            f"building_prevalence[{clazz}]"
+            for clazz in range(self.building_categories_count)
+        ]
+        self.prevalences = pd.DataFrame(values, columns=columns).set_index(
+            "street_index"
+        )
+
+    def point_level(self):
+        point_data = self._sightline_indicators[
+            [
+                "sightline_points",
+                "left_os_count",
+                "left_os",
+                "left_sb_count",
+                "left_sb",
+                "left_h",
+                "left_hw",
+                "left_bc",
+                "right_os_count",
+                "right_os",
+                "right_sb_count",
+                "right_sb",
+                "right_h",
+                "right_hw",
+                "right_bc",
+                "front_sb",
+                "back_sb",
+            ]
+        ]
+        point_data = point_data.explode(point_data.columns.tolist())
+        for col in point_data.columns[1:]:
+            point_data[col] = pd.to_numeric(point_data[col])
+
+        inds = [
+            "os_count",
+            "os",
+            "sb_count",
+            "sb",
+            "h",
+            "hw",
+            "bc",
+        ]
+
+        if hasattr(self, "plots"):
+            # process parcel data
+            left_parcel_seq_sb = []
+            left_parcel_seq_sb_depth = []
+            right_parcel_seq_sb = []
+            right_parcel_seq_sb_depth = []
+
+            # we occasionally have more sightlines per point, so we need to average
+            # values
+            for row in self._plot_indicators.itertuples():
+                with warnings.catch_warnings():
+                    warnings.filterwarnings(
+                        "ignore", "Mean of empty slice", RuntimeWarning
+                    )
+                    left_inds = np.cumsum(row.left_parcel_sb_count)[:-1]
+                    left_parcel_seq_sb.append(
+                        [
+                            np.nanmean(x)
+                            for x in np.split(row.left_parcel_seq_sb, left_inds)
+                        ]
+                    )
+                    left_parcel_seq_sb_depth.append(
+                        [
+                            np.nanmean(x)
+                            for x in np.split(row.left_parcel_seq_sb_depth, left_inds)
+                        ]
+                    )
+
+                    right_inds = np.cumsum(row.right_parcel_sb_count)[:-1]
+                    right_parcel_seq_sb.append(
+                        [
+                            np.nanmean(x)
+                            for x in np.split(row.right_parcel_seq_sb, right_inds)
+                        ]
+                    )
+                    right_parcel_seq_sb_depth.append(
+                        [
+                            np.nanmean(x)
+                            for x in np.split(row.right_parcel_seq_sb_depth, right_inds)
+                        ]
+                    )
+
+            point_parcel_data = pd.DataFrame(
+                {
+                    "left_plot_seq_sb": left_parcel_seq_sb,
+                    "left_plot_seq_sb_depth": left_parcel_seq_sb_depth,
+                    "right_plot_seq_sb": right_parcel_seq_sb,
+                    "right_plot_seq_sb_depth": right_parcel_seq_sb_depth,
+                },
+                index=self._plot_indicators.index,
+            )
+            point_data = pd.concat(
+                [
+                    point_data,
+                    point_parcel_data.explode(
+                        point_parcel_data.columns.tolist()
+                    ).astype(float),
+                ],
+                axis=1,
+            )
+            inds.extend(
+                [
+                    "plot_seq_sb",
+                    "plot_seq_sb_depth",
+                ]
+            )
+
+        for ind in inds:
+            if "count" in ind:
+                sums = point_data[[f"left_{ind}", f"right_{ind}"]].sum(axis=1)
+                nan_mask = (
+                    point_data[[f"left_{ind}", f"right_{ind}"]].isna().all(axis=1)
+                )
+                sums[nan_mask] = np.nan
+                point_data[ind] = sums
+            else:
+                point_data[ind] = point_data[[f"left_{ind}", f"right_{ind}"]].mean(
+                    axis=1
+                )
+
+        return point_data.set_geometry(
+            "sightline_points", crs=self.streets.crs
+        ).rename_geometry("geometry")
+
+
+def rotate(x, y, xo, yo, theta):  # rotate x,y around xo,yo by theta (rad)
+    xr = math.cos(theta) * (x - xo) - math.sin(theta) * (y - yo) + xo
+    yr = math.sin(theta) * (x - xo) + math.cos(theta) * (y - yo) + yo
+    return [xr, yr]
+
+
+rad_90 = np.deg2rad(90)
+
+
+def extend_line_end(line, distance):
+    coords = line.coords
+    nbp = len(coords)
+
+    len_ext = distance + 1  # eps
+
+    # extend line start point
+    ax, ay = coords[0]
+    bx, by = coords[1]
+
+    # extend line end point
+    ax, ay = coords[nbp - 1]
+    bx, by = coords[nbp - 2]
+    len_ab = math.sqrt((ax - bx) ** 2 + (ay - by) ** 2)
+    xe = ax + (ax - bx) / len_ab * len_ext
+    ye = ay + (ay - by) / len_ab * len_ext
+    return LineString(coords[0 : nbp - 1] + [[xe, ye]])
+
+
+def lines_angle(l1, l2):
+    v1_a = l1.coords[0]
+    v1_b = l1.coords[-1]
+    v2_a = l2.coords[0]
+    v2_b = l2.coords[-1]
+    start_x = v1_b[0] - v1_a[0]
+    start_y = v1_b[1] - v1_a[1]
+    dest_x = v2_b[0] - v2_a[0]
+    dest_y = v2_b[1] - v2_a[1]
+    ahab = math.atan2((dest_y), (dest_x))
+    ahao = math.atan2((start_y), (start_x))
+
+    ab = ahab - ahao
+    # calc radian
+    if ab > math.pi:
+        angle = ab + (-2 * math.pi)
+    else:
+        if ab < 0 - math.pi:
+            angle = ab + (2 * math.pi)
+        else:
+            angle = ab + 0
+
+    return math.degrees(angle)
diff --git a/momepy/tests/test_streetscape.py b/momepy/tests/test_streetscape.py
new file mode 100644
index 00000000..bc4857f4
--- /dev/null
+++ b/momepy/tests/test_streetscape.py
@@ -0,0 +1,357 @@
+import geopandas as gpd
+import numpy as np
+import pytest
+
+import momepy
+
+
+class TestStreetscape:
+    def setup_method(self):
+        self.streets = gpd.read_file(
+            momepy.datasets.get_path("bubenec"), layer="streets"
+        ).to_crs(5514)
+        self.buildings = gpd.read_file(
+            momepy.datasets.get_path("bubenec"), layer="buildings"
+        ).to_crs(5514)
+        self.plots = gpd.read_file(
+            momepy.datasets.get_path("bubenec"), layer="plots"
+        ).to_crs(5514)
+
+        self.buildings["category"] = np.repeat(
+            np.arange(0, 6), self.buildings.shape[0] // 6
+        )
+        self.buildings["height"] = np.linspace(12, 30, self.buildings.shape[0])
+
+    def test_minimal(self):
+        sc = momepy.Streetscape(self.streets, self.buildings)
+
+        street_df = sc.street_level()
+        point_df = sc.point_level()
+
+        assert street_df.shape == (35, 75)
+        assert point_df.shape == (1277, 24)
+
+    def test_no_dtm(self):
+        sc = momepy.Streetscape(self.streets, self.buildings)
+        sc.compute_plots(self.plots)
+
+        street_df = sc.street_level()
+        point_df = sc.point_level()
+
+        assert street_df.shape == (35, 92)
+        assert point_df.shape == (1277, 30)
+
+    def test_no_plots(self):
+        rioxarray = pytest.importorskip("rioxarray")
+
+        dtm = rioxarray.open_rasterio(momepy.datasets.get_path("bubenec"), layer="dtm")
+        sc = momepy.Streetscape(self.streets, self.buildings)
+        sc.compute_slope(dtm)
+
+        street_df = sc.street_level()
+        point_df = sc.point_level()
+
+        assert street_df.shape == (35, 79)
+        assert point_df.shape == (1277, 24)
+
+    def test_all_values(self):
+        rioxarray = pytest.importorskip("rioxarray")
+
+        dtm = rioxarray.open_rasterio(momepy.datasets.get_path("bubenec"), layer="dtm")
+        sc = momepy.Streetscape(
+            self.streets, self.buildings, category_col="category", height_col="height"
+        )
+        sc.compute_plots(self.plots)
+        sc.compute_slope(dtm)
+
+        street_df = sc.street_level()
+        point_df = sc.point_level()
+
+        assert street_df.shape == (35, 102)
+        assert point_df.shape == (1277, 30)
+
+        np.testing.assert_allclose(
+            street_df.drop(columns="geometry").median().to_numpy(),
+            [
+                40.0,
+                1.0,
+                5.0,
+                49.52091382053271,
+                40.36256501570919,
+                69.83386039161877,
+                3.0300070452496355,
+                12.059253149635389,
+                10.21810727846115,
+                0.9342180499612095,
+                8.235235059925058,
+                5.466598786795851,
+                50.0,
+                50.0,
+                71.7417882891403,
+                0.47908617946728604,
+                5.219298794549362,
+                3.6007964573641646,
+                40.487349637520765,
+                21.087695047127575,
+                13.986169016184006,
+                0.0,
+                0.10383427236125595,
+                0.5186793716655227,
+                0.0,
+                0.08919263116228532,
+                0.39624778346488937,
+                40.513264598873654,
+                20.579992651038328,
+                11.52970982277211,
+                0.0,
+                0.08891354078509374,
+                0.39268806547246066,
+                12.272727272727275,
+                13.309090909090907,
+                16.65734265734266,
+                0.0,
+                0.0,
+                0.111635608832882,
+                0.6678662504155345,
+                0.9123068730113607,
+                1.28077102705215,
+                0.0,
+                0.017397828672440434,
+                0.05759654889470471,
+                139.05332097291637,
+                600.0,
+                0.0,
+                40.0,
+                7.628569795201426,
+                1.3404001620798687,
+                0.36363636363636365,
+                0.898989898989899,
+                0.0,
+                0.0,
+                0.9420168067226891,
+                0.8903903903903904,
+                0.36363636363636365,
+                0.898989898989899,
+                0.0,
+                0.0,
+                0.932967032967033,
+                0.8903903903903904,
+                0.007707360211947403,
+                0.022279193333061414,
+                0.04070216572227663,
+                0.006257085035661018,
+                0.1269755497050381,
+                0.19715820572615364,
+                0.0,
+                0.5,
+                0.5,
+                120.32016819730187,
+                3.97464175672102e-05,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                0.0,
+                35.0,
+                35.0,
+                10.200056087159462,
+                10.531177039571478,
+                9.65723343618015,
+                0.0854689801885757,
+                0.0466194437392231,
+                0.03884953644935259,
+                25.560049303845382,
+                24.0199153637509,
+                24.007298122345833,
+                1.3470143628325295,
+                1.0986002812885625,
+                1.2473399517104464,
+                0.18589437276314322,
+                0.18666557494673167,
+                0.2044865211771481,
+                0.5716914086061647,
+                0.009978565533223297,
+                40.0,
+                1.0,
+            ],
+        )
+
+        np.testing.assert_allclose(
+            point_df.drop(columns="geometry").median().to_numpy(),
+            [
+                1.0,
+                50.0,
+                0.0,
+                10.654982043105191,
+                19.111888111888113,
+                1.4711633374979423,
+                0.0,
+                1.0,
+                50.0,
+                0.0,
+                10.535728185318764,
+                19.04895104895105,
+                1.7192769653490183,
+                0.0,
+                300.0,
+                300.0,
+                9.463190880206646,
+                24.97048079338272,
+                9.296934793860625,
+                23.159005973830855,
+                2.0,
+                37.11048755809363,
+                1.0,
+                10.65171713340952,
+                19.3006993006993,
+                1.7635576674363311,
+                6.4329505145817825,
+                9.885960548533543,
+                24.735688410357433,
+            ],
+        )
+
+        assert (
+            street_df.columns
+            == [
+                "N",
+                "n_l",
+                "n_r",
+                "left_os",
+                "right_os",
+                "os",
+                "left_os_std",
+                "right_os_std",
+                "os_std",
+                "left_os_mad",
+                "right_os_mad",
+                "os_mad",
+                "left_os_med",
+                "right_os_med",
+                "os_med",
+                "left_os_mad_med",
+                "right_os_mad_med",
+                "os_mad_med",
+                "left_sb",
+                "right_sb",
+                "sb",
+                "left_sb_std",
+                "right_sb_std",
+                "sb_std",
+                "left_sb_mad",
+                "right_sb_mad",
+                "sb_mad",
+                "left_sb_med",
+                "right_sb_med",
+                "sb_med",
+                "left_sb_mad_med",
+                "right_sb_mad_med",
+                "sb_mad_med",
+                "left_h",
+                "right_h",
+                "H",
+                "left_h_std",
+                "right_h_std",
+                "H_std",
+                "left_hw",
+                "right_hw",
+                "HW",
+                "left_hw_std",
+                "right_hw_std",
+                "HW_std",
+                "csosva",
+                "tan",
+                "tan_std",
+                "n_tan_ratio",
+                "tan_ratio",
+                "tan_ratio_std",
+                "par_tot",
+                "par_rel",
+                "left_par_tot",
+                "right_par_tot",
+                "left_par_rel",
+                "right_par_rel",
+                "par_tot_15",
+                "par_rel_15",
+                "left_par_tot_15",
+                "right_par_tot_15",
+                "left_par_rel_15",
+                "right_par_rel_15",
+                "left_built_freq",
+                "right_built_freq",
+                "built_freq",
+                "left_built_coverage",
+                "right_built_coverage",
+                "built_coverage",
+                "nodes_degree_1",
+                "nodes_degree_4",
+                "nodes_degree_3_5_plus",
+                "street_length",
+                "windingness",
+                "building_prevalence[0]",
+                "building_prevalence[1]",
+                "building_prevalence[2]",
+                "building_prevalence[3]",
+                "building_prevalence[4]",
+                "building_prevalence[5]",
+                "left_plot_count",
+                "right_plot_count",
+                "plot_sb",
+                "left_plot_sb",
+                "right_plot_sb",
+                "plot_freq",
+                "left_plot_freq",
+                "right_plot_freq",
+                "plot_depth",
+                "left_plot_depth",
+                "right_plot_depth",
+                "plot_WD_ratio",
+                "left_plot_WD_ratio",
+                "right_plot_WD_ratio",
+                "plot_WP_ratio",
+                "left_plot_WP_ratio",
+                "right_plot_WP_ratio",
+                "slope_degree",
+                "slope_percent",
+                "n_slopes",
+                "slope_valid",
+                "geometry",
+            ]
+        ).all()
+
+        assert (
+            point_df.columns
+            == [
+                "geometry",
+                "left_os_count",
+                "left_os",
+                "left_sb_count",
+                "left_sb",
+                "left_h",
+                "left_hw",
+                "left_bc",
+                "right_os_count",
+                "right_os",
+                "right_sb_count",
+                "right_sb",
+                "right_h",
+                "right_hw",
+                "right_bc",
+                "front_sb",
+                "back_sb",
+                "left_plot_seq_sb",
+                "left_plot_seq_sb_depth",
+                "right_plot_seq_sb",
+                "right_plot_seq_sb_depth",
+                "os_count",
+                "os",
+                "sb_count",
+                "sb",
+                "h",
+                "hw",
+                "bc",
+                "plot_seq_sb",
+                "plot_seq_sb_depth",
+            ]
+        ).all()