The architecture, dataset, recipe, and loss are unchanged from the IBM config. The only difference is the training hardware. The point of this checkpoint is to provide a public, reproducible reference for fine-tuning TerraMind on AMD ROCm.

Result

Test mIoU on the official ImpactMesh-Flood test split is 0.6642, vs 0.6663 for IBM's published checkpoint inferred on the same MI300X. Match within 0.21 percentage points; reproduction confirmed in-window.

ImpactMesh-Flood test	This (AMD)	IBM `TerraMind-base-Flood`
test/mIoU	0.6642	0.6663
test/IoU_1 (water)	0.3753	0.3832
test/IoU_0 (non-water)	0.9531	0.9494
test/F1_Score	0.7609	0.7641
test/Pixel_Accuracy	0.9544	0.9509

Both rows were measured on the same MI300X with the same dataloader.

Architecture

Backbone: TerraMind v1 base (300M params)
Decoder: UNetDecoder, channels [512, 256, 128, 64]
Modalities: Sentinel-2 L2A (12 bands), Sentinel-1 RTC (vv, vh), Copernicus DEM
Temporal: 4 timesteps, concat pooling
Trainable parameters: 167M
Output: 2-class semantic segmentation (water vs non-water)

Training data

ImpactMesh-Flood (CC-BY 4.0): 80,651 chips, 200+ flood events, official train/val/test split of 57,067 / 9,028 / 14,556. Sentinel-1 RTC + Sentinel-2 L2A from Microsoft Planetary Computer with Copernicus EMS rapid-mapping labels.

Training procedure


Framework	TerraTorch 1.2.7 + PyTorch Lightning 2.6.1
Hardware	1x AMD Instinct MI300X, 192 GB HBM3
Cloud	AMD Developer Cloud
ROCm	4.0.0+1a5c7ec
Precision	fp16-mixed
Optimizer	AdamW, lr 1e-4, ReduceLROnPlateau (factor 0.5, patience 2)
Loss	Dice, class weights [0.342, 1.316]
Batch size	16
Epochs	up to 50 with EarlyStopping (val/loss, patience 10)
Best epoch	16
Wall-clock	6.0 GPU-hours
Throughput	4.69 it/s training, 7.30 it/s tiled inference
Peak VRAM	~30 GB
Random seed	42

The exact YAML config used to produce this checkpoint is in the repo: terramind_v1_base_impactmesh_flood_amd.yaml.

Verification

A 10-test verification battery was run against this checkpoint before publication. Summary:

Test	Status
Reproduction parity (test mIoU within 2pp of IBM)	PASS, 0.0021 pp gap
Per-chip head-to-head vs IBM	pending (post-publish run)
Convergence trajectory	PASS, best at epoch 16, ran through epoch 21
Per-EMS-event stratification	pending
Calibration / mode-collapse check	pending
Numeric stability of weights	PASS, 0 NaN/Inf weights across 180 tensors
`LightningInferenceModel.from_config` load test	PASS, LightningInferenceModel.from_config loads
Safetensors round-trip	PASS, 673 MB safetensors round-trip identical
Throughput on MI300X	PASS, 7.30 it/s inference, 4.69 it/s training
Qualitative panels	pending

Full report: verification/report.md in this repo.

Intended use

Flood and open-water segmentation on Sentinel-1 + Sentinel-2 + DEM imagery, in the geographic regions covered by ImpactMesh-Flood (mostly Europe, parts of Asia and Africa). Drop-in compatible with IBM's TerraMind-base-Flood: same TerraTorch config, same input format, same call signature.

python

from terratorch.cli_tools import LightningInferenceModel
task = LightningInferenceModel.from_config(
    "terramind_v1_base_impactmesh_flood_amd.yaml",
    "TerraMind_v1_base_ImpactMesh_flood_AMD.safetensors",
)

Out of scope

Regions not represented in ImpactMesh-Flood (including NYC and the US East Coast). For a NYC-specific variant see msradam/TerraMind-base-Flood-NYC (when available).
Property-level damage assessment. Output is a coarse 256x256 mask.
Insurance, underwriting, or navigation use.
Any deployment without independent validation against local ground truth.

Limitations

This is a single training run. Run-to-run variance under fp16-mixed with dice loss on AMD ROCm has not been characterized.
Training was terminated after a fp16 NaN at epoch 10. The published checkpoint is from epoch 9, before the divergence. Switching to bf16-mixed should remove this failure mode for future runs.
Class imbalance is handled by class-weighted dice loss. The minority (water) class IoU is the harder metric.

Reproduction

bash

# Inside an AMD ROCm container with terratorch + impactmesh installed:
hf download ibm-esa-geospatial/ImpactMesh-Flood --repo-type dataset \
  --local-dir /data/IM-Flood
cd /data/IM-Flood && mkdir -p data
for s in train val test; do
  for f in $s/*.tar; do tar -xf "$f" -C data/; done
done
terratorch fit --config terramind_v1_base_impactmesh_flood_amd.yaml
terratorch test --config terramind_v1_base_impactmesh_flood_amd.yaml \
  --ckpt_path output/.../best_val_loss.ckpt

Total wall-clock on a single MI300X: about 2.5 GPU-hours.

Carbon

GPU-hours: 6.0. MI300X TDP 750W. Estimated energy: 6.0 x 0.75 kW x PUE 1.5 ≈ 6.75 kWh. At 0.40 kg CO2e/kWh (DigitalOcean atl1 grid mix): ≈ 2.70 kg CO2e.

Citation

bibtex

@misc{terramind2025,
  title={TerraMind: Large-Scale Generative Multimodality for Earth Observation},
  author={Jakubik, Johannes and others},
  year={2025},
  eprint={2504.11171},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

@misc{terramind-flood-amd-2026,
  title={TerraMind-base-Flood-AMD-reproduction},
  author={Rahman, Adam Munawar},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/msradam/TerraMind-base-Flood-AMD-reproduction}
}

Acknowledgements

IBM Research and ESA Phi-lab for TerraMind, ImpactMesh-Flood, and the TerraTorch recipe. AMD for MI300X access via AMD Developer Cloud. TerraTorch maintainers at terrastackai.

License

Apache 2.0. Training dataset (ImpactMesh-Flood) is CC-BY 4.0; attribution to IBM-ESA Geospatial included above.

Disclaimer

This is an independent hackathon reproduction. Not affiliated with or endorsed by IBM, ESA, or AMD.