earthchange
Multipurpose satellite change detection in pure Python. Map deforestation, mining, urbanisation, floods, burns, surface-water change and multi-epoch urban growth anywhere on Earth from free Sentinel-1/2 and Landsat data — via Google Earth Engine or Microsoft Planetary Computer (no account needed). Export georeferenced GeoTIFFs, quick-look PNGs, statistics, and print-ready A4 maps.
- 📖 Full hands-on tutorial (English & Bahasa Indonesia): https://firmanhadi21.github.io/rs-change-detection/
- 💻 Source, examples & case study: https://github.com/firmanhadi21/rs-change-detection
Install
Heavy dependencies are optional extras, so the install stays lean:
pip install 'earthchange[gee]' # Google Earth Engine backend (free account)
pip install 'earthchange[mpc,maps]' # Planetary Computer + maps (no account)
pip install 'earthchange[all]' # everything
Quick start
# Deforestation around a coordinate, with a finished map
earthchange -s deforestation --lat -3.333 --lon 122.25 --radius 6 --map
# Flood extent from Sentinel-1 SAR — no Earth Engine account needed
earthchange -s flood --lat 27.2 --lon 68.3 \
--pre 2022-07-01:2022-07-25 --post 2022-08-20:2022-09-10 --backend mpc
# Urban growth timing across 2010/2015/2020 (Landsat 5/8/9)
earthchange -s urban-trend --lat -6.30 --lon 107.15 --map
# List everything
earthchange --list
Each run writes a self-contained output/<run-id>/ folder containing the PNG,
GeoTIFF, statistics JSON, metadata, and any maps.
Scenarios
| Scenario | Method | Sensor |
|---|---|---|
deforestation |
NDVI loss | Sentinel-2 |
urbanization |
Built-up gain — NDBI (default), UI, BU, IBI, or thermal NDISI/EBBI via --method |
Sentinel-2 / Landsat |
water |
NDWI change | Sentinel-2 |
burn |
dNBR severity | Sentinel-2 |
mining |
SIRAD radar temporal + NDVI loss | Sentinel-1 + S2 |
flood |
SAR water extent (event vs baseline) | Sentinel-1 |
urban-trend |
NDBI at 3 epochs → RGB growth-timing map | Landsat 5/8/9 |
coastline |
Sea boundary + shoreline change (erosion/accretion) + retreat rate m/yr | S1 / S2 / Landsat |
transit-access |
% population with access to public transport (SDG 11.2.1) | WorldPop + OSM |
island-heat |
SST + LST + wet-bulb (humid heat) trends for small islands | OISST / Landsat / ERA5 |
urban-heat |
Urban heat island (SUHII) + hot-spot map + decadal trend | GHSL + Landsat + MODIS |
forest-history |
Multi-epoch deforestation: year-of-loss map + forest-area trajectory | S2 / Landsat NDVI |
population-change |
Two-epoch population change: Miloš-style spike poster + map + optional true GPU 3D via forge3d | GHSL GHS_POP |
haze |
Smoke & air quality during fires: PM2.5 (ISPU), aerosol index, hotspots | CAMS + Sentinel-5P + FIRMS |
fire-history |
Multi-year fire record: burned area per year (peat vs mineral), recurrence map, fire season | MODIS MCD64A1 + FIRMS |
Two backends
--backend |
Data source | Account? |
|---|---|---|
gee (default) |
Google Earth Engine | free account + earthengine authenticate |
mpc |
Microsoft Planetary Computer (STAC) | none — streams COGs, processes locally |
Optical scenarios build cloud-masked median composites; radar scenarios auto-select the Sentinel-1 orbit with coverage. The AOI is a square centred on your coordinate. Landsat 7 is skipped (SLC-off gaps).
Make maps from a finished run
earthmap output/<run-id> # render A4 map sheets offline (no GEE)
Map sheets include an OpenStreetMap basemap, the change layer, legend, a statistics panel, a location inset, coordinate grid, scale bar and north arrow.
License
MIT © Firman Hadi. Data: Copernicus Sentinel (ESA) and Landsat (USGS/NASA), via Google Earth Engine or Microsoft Planetary Computer.
1 maintainer
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