PyroNear Visibility Analysis

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

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Overview

This repository provides a script-based solution for evaluating viewsheds (areas visible from high points), calculating coverage, and analyzing overlaps between them. It integrates with QGIS and uses raster-based elevation data.

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Contents

• ./data: Input and output data (DEM files, input CSV, output CSV).
• ./src: All Python scripts.
  • viewshed.py: Creates viewsheds from high points.
  • area_analysis.py: Computes coverage metrics and overlaps.
  • utils.py: General-purpose functions (I/O, normalization, display).
• ./viewsheds_geotiff: Raw viewsheds generated by the tool.
• ./normalized: Viewsheds after NaN removal.
• ./fusion: Viewshed files resulting from fusion operations (OR/AND).
• main.py: The main script to run the analysis.

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Requirements

• QGIS (https://www.qgis.org/en/site/forusers/alldownloads.html)
• Plugins:
  • SRTM Downloader (for DEM files)
  • HCMGIS (for base maps)
• EarthData account for DEM data: https://urs.earthdata.nasa.gov/users/new

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DEM Preparation (QGIS)

1. Install plugins: SRTM Downloader and HCMGIS.
2. Set map to Google Maps: HCMGIS > BaseMaps > Google Maps.
3. Use SRTM Downloader:
   • Either set canvas extent or input coordinates manually.
   • Login to EarthData when prompted.
4. Merge DEM tiles: Use "Merge" tool.
   • Output format: Int16
   • Save as: ./data/dem_file.tif
5. Reproject the DEM:
   • Use "Warp (Reproject)" tool.
   • Target CRS: UTM zone for your area (e.g., EPSG:32631)
   • Save as: ./data/dem_file_projected.tif

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Running the Code (QGIS)

1. Open QGIS > Python Console > Show Editor
2. Load main.py
3. Run the script using the "Play" button

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Functionality

Viewshed Generation (viewshed.py)

• Reads a CSV of observation points (lat/lon, height, name).
• Generates a viewshed (visibility map) for each point.
• Reprojects to appropriate CRS.
• Applies a visual style (.qml).
• Saves results as individual .tif files.

Normalization (utils.py)

• Removes NaN values from .tif files (required for further analysis).
• Output files are saved to ./normalized.

Fusion (utils.py)

• Logical fusion of viewsheds using OR or AND.
• Results saved to ./fusion.

Coverage Analysis (area_analysis.py)

• Calculates:
  • Area covered by a single viewshed
  • Coverage ratio relative to the network
  • Overlap between two viewsheds

Output

• Final metrics are stored in ./data/output.csv.

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Configuration

Paths are defined in main.py:

• VISILITY_ANALYSIS_PATH: Root folder (./analysis_shape)
• DATA_PATH: ./data
• CSV_PATH: Input file (pts_hauts_x.csv)
• DEM_PROJECTED_PATH: dem_file_projected.tif
• VIEWSHEDS_PATH, NORM_VIEWSHEDS_PATH, FUSION_PATH: Subdirectories for outputs
• OUTPUT_PATH: ./data/output.csv

(You can externalize paths into a config.py file if needed.)

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Dependencies

Python Standard Library:

• os, csv, numpy

Image Processing:

• PIL.Image, PIL.TiffTags

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Usage Summary

1. Prepare DEM using QGIS.
2. Normalize all viewsheds using normalize_create().
3. Generate KPIs using covered_surface().
4. Output will be saved in output.csv.

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Notes

• Viewshed generation uses QGIS native plugin Visibility Analysis.
• Normalization is required because the default plugin outputs use NaN for no-data values.
• QGIS may misinterpret the scale of .tif files unless color ramps are explicitly set to binary.

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