https://github.com/pyronear/pyro-visibility-analysis

Tools to perform visibility analysis
https://github.com/pyronear/pyro-visibility-analysis

Last synced: about 1 year ago
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Tools to perform visibility analysis

• Host: GitHub
• URL: https://github.com/pyronear/pyro-visibility-analysis
• Owner: pyronear
• License: apache-2.0
• Created: 2024-10-30T09:36:44.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-10T17:53:36.000Z (about 1 year ago)
• Last Synced: 2025-04-22T11:23:44.277Z (about 1 year ago)
• Language: Python
• Size: 30.8 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

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README

          
# 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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