diff --git a/doc/tutorial/climada_entity_Exposures_osm.ipynb b/doc/tutorial/climada_entity_Exposures_osm.ipynb
index d2f512b57..baae15d04 100644
--- a/doc/tutorial/climada_entity_Exposures_osm.ipynb
+++ b/doc/tutorial/climada_entity_Exposures_osm.ipynb
@@ -20,9 +20,9 @@
    "metadata": {},
    "source": [
     "## Quick example\n",
-    "Here we provide a quick example of an impact calculation with CLIMADA and OSM data.\n",
+    "Here we provide a quick example of an impact calculation with CLIMADA and OpenStreetMap (OSM) data.\n",
     "We use in this example main roads in Honduras as exposures, and historical tropical cyclones\n",
-    "as hazard. We load the osm data using `osm-flex` and disaggregate the exposures, \n",
+    "as hazard. We load the OSM data using `osm-flex` and disaggregate the exposures, \n",
     "compute the damages, and reaggregate the exposures to their original shape using the \n",
     "function `calc_geom_impact` from the util module `lines_polys_handler`. \n",
     "For more details on the `lines_polys_handler` module, please refer to the [documentation](https://climada-python.readthedocs.io/en/latest/tutorial/climada_entity_Exposures_polygons_lines.html)."
@@ -65,7 +65,7 @@
    "metadata": {},
    "source": [
     "The first step is to download a raw osm.pbf file (\"data dump\") for Honduras from geofabrik.de \n",
-    "and extract the layer of interest (here roads), but other layers can be calculated (see CLIMADA and OpenStreetMap [section](#climada-and-openstreetmap))"
+    "and extract the layer of interest (here roads). See the set-up CLIMADA exposures from OpenStreetMap [section](#set-up-climada-exposures-from-openstreetmap) for more details."
    ]
   },
   {
@@ -148,12 +148,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
     "# disaggregate in the same CRS as the exposures are defined (here meters), resolution 500m\n",
-    "# divide values on points\n",
+    "# replicate values on points\n",
     "# aggregate by summing\n",
     "\n",
     "impact = u_lp.calc_geom_impact(\n",
@@ -224,12 +224,13 @@
     "3. **Pre-process**; apply pre-processing steps as e.g. clipping, simplifying, or reprojecting the retrieved layer.\n",
     "4. **Cast** the geodataframe into a CLIMADA `Exposures` object.\n",
     "5. **Disagreggate** complex shapes exposures into points for impact calculation.\n",
+    "\n",
     "Once those 5 steps are completed, one can proceed with the impact calculation. For more details on \n",
     "how to use lines and polygons as exposures within CLIMADA, please refer to the [documentation](https://climada-python.readthedocs.io/en/latest/tutorial/climada_entity_Exposures_polygons_lines.html).\n",
     "\n",
-    "In the following, we illustrate how to obtain different exposures types such as healthcare facilities,\n",
-    "road networks, or forests and how to use them within CLIMADA as points, lines,\n",
-    "and polygons exposures. We also briefly illustrate the use of the `clip` and `simplify` modules\n",
+    "In the following, we illustrate how to obtain different exposures types such as forests or \n",
+    "healthcare facilities, and how to use them within CLIMADA as points, lines,\n",
+    "and polygons exposures. We also briefly illustrate the use of the `simplify` module\n",
     "available within the `osm-flex` package."
    ]
   },
@@ -239,7 +240,7 @@
    "source": [
     "#### Download a raw osm.pbf file (\"data dump\")\n",
     "First, we need to select a specific country and download its data from geofabrik.de. It is possible to\n",
-    "download data from specific countries using iso3 codes or for regions directly."
+    "download data from specific countries using iso3 codes or from regions directly."
    ]
   },
   {
@@ -269,8 +270,8 @@
     "#### Extract the features of interest\n",
     "\n",
     "We next extract the exposures data of interest from OSM using the `extract()` method which\n",
-    "allows us to query any tags available on openstreetmap.\n",
-    "Two variables have to be specified: `osm_keys`, a list with all the columns to report in the gdf, and `osm_query`, a string of key-value constraints to apply during the search. We illustrate its use by querying the download of forests for Honduras."
+    "allows us to query any tags available on OpenStreetMap.\n",
+    "Two variables have to be specified: `osm_keys`, a list with all the columns to report in the GeoDataFrame, and `osm_query`, a string of key-value constraints to apply during the search. We illustrate its use by querying the download of forests for Honduras."
    ]
   },
   {