PyroNear Visibility Analysis

Automated analysis of coverage and visibility zones using QGIS and Python.

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🚀 How to analyze sites

1. Create a folder data/sdis-xx/

2. Add the site info in data/sdis-xx/sites.csv (semicolon-delimited):

   Name;Latitude;Longitude;Height
   Site A;48.8566;2.3522;30
   Site B;48.9000;2.4000;30

3. Set the region in config.py: REGION = "sdis-xx" (must match the folder name under data/). All scripts read it from there.

4. Generate the DEM: uv run python generate_dem.py

5. Open QGIS, then in the Python Console run main.py

6. Export the results: uv run python export.py

7. Visualize: uv run streamlit run visualize.py

See the sections below for details.

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🔍 Overview

This repository provides a script-based solution to:

• Generate viewsheds (visibility zones) from high points
• Normalize and combine them
• Analyze coverage and overlaps
• Visualize everything within QGIS using real DEM data and basemaps

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📁 Project Structure

.
├── data/                    # Input & output data (CSV, DEM, results)
│   ├── sdis-77/
│   │   ├── sites.csv        # Input coordinates
│   │   ├── dem_l93.tif      # Projected DEM (EPSG:2154)
│   │   └── output/          # Generated outputs (viewsheds, metrics)
├── analysis_shape/         # Core Python modules
│   ├── viewshed.py          # Viewshed creation
│   ├── area_analysis.py     # Coverage & overlap analysis
│   └── utils.py             # Helper functions
├── generate_dem.py         # Script to download and reproject DEM
├── main.py                 # Main QGIS execution script
├── export.py               # Export viewsheds as KMZ overlays + GeoPackage polygons
├── visualize.py            # Streamlit app to explore viewsheds + per-site/total area stats
├── viewshed_style.qml      # QGIS raster style applied to viewsheds
└── README.md

📦 Installation

Dependencies are managed with uv — no manual environment setup needed. uv run resolves and installs everything declared in pyproject.toml into an isolated environment on first run.

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🌍 Step 1: Automatic DEM Generation

You can automatically download and project a DEM (Digital Elevation Model):

• Set REGION in config.py to the region you want to analyse.
• Then run:

uv run python generate_dem.py

This script:

• Parses station coordinates from sites.csv
• Computes a bounding box with buffer
• Downloads SRTM tiles using the eio CLI
• Reprojects to EPSG:2154
• Saves dem_l93.tif in the folder where the provided CSV is stored

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🛰️ Step 2: Running the Analysis in QGIS

1. Set REGION in config.py to the region you want to analyse.
2. Open QGIS.
3. Install the QGIS plugin "Visibility Analysis" (by Zoran Čučković) if you don't have it.
4. Create a new project and save it in the pyro-visibility-analysis working directory (this avoids path issues later).
5. Open the Python Console → Show Editor.
6. If analysis_shape can't be imported, register the repo path first:

   import sys
   sys.path.append("/PATH_TO_THE_REPOSITORY/pyro-visibility-analysis")

7. Load and run main.py.
8. Go to Project → Properties → CRS and set it to EPSG:2154 (Lambert-93).

ℹ️ Note: Due to QGIS limitations, the project CRS might not fully apply during script execution. Manually setting it ensures all layers are correctly reprojected.

This will:

• Load your DEM and OpenStreetMap as background
• Generate and normalize viewsheds
• Compute overlaps and total coverage
• Save results to output.csv

To share the project, share the QGIS project file (.qgz) together with all data in the directory of the CSV you provided.

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📤 Outputs

After running main.py, you'll find (in the output/ folder under your region, e.g. for sdis-77):

• Individual viewsheds: data/sdis-77/output/viewsheds_geotiff/
• Normalized viewsheds: data/sdis-77/output/normalized/
• Combined viewsheds: data/sdis-77/output/fusion/
• Coverage metrics: data/sdis-77/output/output.csv

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📦 Export & Visualization

Once main.py has produced the raw viewsheds, two helper scripts package them for delivery and quick inspection. Both read the REGION set in config.py.

export.py

uv run python export.py

For each viewshed_<site>.tif in data/<region>/output/viewsheds_geotiff/:

• KMZ — reprojects to WGS84, renders the visible pixels as a colored semi-transparent PNG ground overlay, and writes one <site>.kmz per site (Google Earth ready). Output: kmz_output_<region>/.
• GeoPackage — polygonizes each binary raster into a single dissolved (Multi)Polygon per site (vectorization in source CRS for accuracy, then reprojected to WGS84), and writes them all to a single kmz_output_<region>/viewsheds_<region>.gpkg file with one layer (viewsheds) and a site_name attribute.

visualize.py (Streamlit app)

uv run streamlit run visualize.py

Interactive web app to inspect the GeoPackage produced by export.py.

Sidebar

• Sites — All / None bulk toggles, then a checkbox per site.
• Basemap — switch between OpenStreetMap and Satellite (Esri imagery).

Main panel

• Top-of-page metrics: number of sites selected, total covered area (geometric union — overlap deduplicated), and the overlap surface (sum of per-site areas minus the union).
• A folium map of the selected viewsheds (colored by site, hover tooltip with site name + area) next to a sortable per-site area table (km²).

Areas are computed by reprojecting to a local UTM zone via gdf.estimate_utm_crs() so they are accurate regardless of region.

Requires streamlit, streamlit-folium, folium, matplotlib, mapclassify (all declared in pyproject.toml).

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🧠 Functionality

viewshed.py

• Reads sites.csv with Name, Latitude, Longitude, Height
• Reprojects points to EPSG:2154
• Generates one .tif viewshed per point

utils.py

• normalize_create() replaces no-data with zeros
• display_tif() adds raster layer with style
• fusion_or() and fusion_and() combine rasters

area_analysis.py

• Uses rasterio to compute:
  • Area covered per viewshed
  • % of total coverage
  • Pairwise overlaps between viewsheds

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📝 Notes

• This project uses the QGIS Visibility Analysis processing tool under the hood.
• Basemaps are OpenStreetMap or Esri satellite imagery (via XYZ tiles).
• All coordinates are reprojected from EPSG:4326 (lat/lon) to EPSG:2154.
• Output rasters use LZW compression for performance.