Live app: https://gfdrr.github.io/sirs-mapillary/ (school + coverage layers load from open data; Mapillary photo layers go through a token proxy - see DEPLOY.md)
A lightweight web map showing where Mapillary street-level photos already exist near schools, for the School Infrastructure Risk Screening (SIRS) work across five West African countries: Niger, Mali, Guinea, Benin, Ghana.
The goal is to assess whether free, existing street-level imagery can help read building characteristics (roof, height, structural type) from the ground - before commissioning any new photo collection.
- Coverage: how much of each country's school network has nearby Mapillary photos, and how recent they are.
- Photos: click a covered school to see the most useful nearby images (camera-facing-the-school first), and open a panoramic viewer.
School locations shown are from OpenStreetMap (open data, ODbL). Street-level imagery is from Mapillary.
It is a single static page - no build step.
# Optional: for the photo layers, provide a Mapillary token
cp .env.example .env # then add your MAPILLARY_TOKEN
python3 -m http.server 8765
# open http://localhost:8765/For a public deployment the Mapillary token must not sit in the browser. A small
Cloudflare Worker proxy holds the token server-side. See DEPLOY.md
for the full steps (deploy the worker, set the proxy base, publish to GitHub Pages).
- School points: © OpenStreetMap contributors, ODbL 1.0.
- Street-level imagery: Mapillary (CC-BY-SA).
- Coverage figures in
data/coverage_summary.jsonare aggregate, country-level only.
This project was developed with significant assistance from AI coding tools.
- Claude Code (Anthropic) - code generation, architecture, and documentation.
- All functionality has been tested and verified to work as intended.
- Features and infrastructure choices have been reviewed and approved by the maintainer.