This first release provides models and datasets for wildfire risk estimation. Among others, this introduces a D-5 wildfire risk inference for French regional areas.
Note: pyro_risks mainly requires pandas, geopandas and scikit-learn , while the API only requires fastapi and uvicorn
Highlights
Datasets
Various datasets with entries indexed by location and time of measurement.
New
- Added NOAA & BDIFF (#6), NASAFIRMS (#7), FWI (#10), ERA5Land (#13), NASAVIIRS (#18), MergedEraFwiViirs (#22), ERA5T (#24, #29) datasets
- Added dataset merging utilities (#9, #13), and closest neighbour aggregation (#13)
- Added data source URL download for modis and ghcn (#4)
- Added live API data fetching (#25)
Models
Trainable models to estimate wildfire risk
New
Documentation
The documentation of the python library
New
- Added Sphinx documentation autobuild (#1)
- Added datasets page (#6)
- Updated README (#1, #16, #27) and added CONTRIBUTING (#16)
Tests
Unittests for the python package
New
- Added unittest for dataset instantiation (#6, #7, #4, #10, #13, #18, #23, #24, #25, #29)
- Added unittests for model inference (#22, #25, #29)
Web Server
Web server made to expose some of the python library features
New
- Added basic FastAPI web server to expose wildfire risk inference (#27)
Others
New
- Added package setup (#1, #6) and CI job (#1, #17)
- Added CI jobs for lint checking, doc building and unittesting (#1, #5, #14)
- Fixed geopandas installation in CI (#6)
- Renamed repo and package (#12)
- Added example scripts (#13, #18, #22, #24)
- Added org funding option
- Added docker orchestration for web server (#27)
- Added Heroku deployment of web server (#27)