impactmesh 0.1.2

ImpactMesh package for mesh-based impact analysis

ImpactMesh

ImpactMesh is a large-scale multimodal, multitemporal dataset for flood and wildfire mapping, released by IBM, DLR, and the ESA Φ-lab. It combines Sentinel-1 SAR, Sentinel-2 optical imagery, and Copernicus DEM with high-quality annotations from Copernicus EMS. The dataset covers over 400 events globally with four temporal observations per event.

Dataset

Please find the wildfire subset at https://huggingface.co/datasets/ibm-esa-geospatial/ImpactMesh-Fire and the flood samples at https://huggingface.co/datasets/ibm-esa-geospatial/ImpactMesh-FLood. The following map gives an overview of events present in ImpactMesh:

This repository provides code to build the pytorch dataloader or directly fine-tune a model with TerraTorch.

Setup

Quick start from pypi:

pip install impactmesh

Alternatively create a new environment and install TerraTorch and the package from source:

python -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install terratorch==1.2.1
pip install zarr==2.18.0 numcodecs==0.15.1
pip install -e .

ImpactMesh uses Zarr Version 2.

Fine-tuning

Run training using on of the configs provided in configs/.

terratorch fit --config configs/terramind_v1_tiny_impactmesh_fire.yaml

Run the evaluation on the test split.

terratorch test --config configs/terramind_v1_tiny_impactmesh_fire.yaml --ckpt output/terramind_tiny_impactmesh_fire/1e-4/best_val_loss.ckpt

Run prediction with the following commands. The predict_data_root is expected to have modalities as subfolders with tif/zarr.zip files similar to ImpactMesh. Add --predict_split <split>.txt with sample prefixes if you only want to predict a subset in predict_data_root. Note that terratorch predict requires DEM input, use run_inference.py as an alternative if you don't use DEM inputs.

terratorch predict -c configs/terramind_v1_tiny_impactmesh_fire.yaml --ckpt path/to/ckeckpoint.pt --predict_output_dir output/impactmesh_fire_predictions --data.init_args.predict_data_root data/fire_inputs

# TerraTorch automatically uses a tiled inference. It might still lead to OOM errors. In that case, you can use:
python impactmesh/run_inference.py -c configs/terramind_v1_tiny_impactmesh_fire.yaml --ckpt path/to/ckeckpoint.pt --output_dir output/impactmesh_fire_predictions

Citation

Our technical report is released soon!

Acknowledgement

ImpactMesh was developed as part of the FAST‑EO project funded by the European Space Agency Φ‑Lab (contract #4000143501/23/I‑DT).

Sentinel-2 Level-2A data were downloaded from Microsoft Planetary Computer and are provided under Copernicus Sentinel license conditions (© European Union 2015–2025, ESA) (https://planetarycomputer.microsoft.com/dataset/sentinel-2-l2a).

Sentinel-1 Radiometrically Terrain Corrected (RTC) SAR data were retrieved from Microsoft Planetary Computer (calibrated to GRD and terrain-corrected using PlanetDEM) under Copernicus Sentinel license terms (© European Union 2014–2025) (https://planetarycomputer.microsoft.com/dataset/sentinel-1-rtc).

The DEM data is produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved.

Annotations were sourced from the Copernicus Emergency Management Service (© European Union, 2012–2025), available at https://emergency.copernicus.eu/.

License

The code and models are release under Apache 2.0. The dataset is released under CC-BY 4.0.