TerraMind-NYC-Adapters

LoRA adapters that specialise ibm-esa-geospatial/TerraMind-1.0-base (IBM-ESA TerraMind 1.0, 1B parameters, multi-modal: Sentinel-2 L2A + Sentinel-1 RTC + Copernicus DEM, four timesteps) on three NYC Earth-observation tasks. Trained on AMD Instinct MI300X via AMD Developer Cloud. Apache-2.0.

GitHub mirror of the model on Hugging Face: huggingface.co/msradam/TerraMind-NYC-Adapters.

Adapters in this family

Adapter	Task	Classes	Card mIoU	This-repo reproduction
buildings_nyc	NYC building-footprint segmentation	2	0.5511	0.365 building IoU at threshold 0.6 (higher than the card's 0.293)
lulc_nyc	NYC 5-class land cover	5	0.5866	0.355 mIoU; water IoU 0.943 (higher than the card's 0.770)
tim_nyc	LULC with Thinking-in-Modalities	5	0.6023	not yet wired in this harness

Each adapter is roughly 325 MB on disk (~5 MB LoRA Δ on attention QKV / proj + ~320 MB UNet decoder trained from scratch). The 1.45 GB TerraMind base sits on disk once and is shared across all adapters.

Demo segmentations

Buildings adapter

Manhattan midtown — model finds essentially every building:

Jamaica Bay — model correctly finds 0.18 % buildings:

Central Park — mixed urban / vegetation:

LULC adapter (5 classes: water / impervious / vegetation / bare / building)

Manhattan midtown — dominantly impervious:

Jamaica Bay — 96 % water:

Central Park — vegetation visible:

Sniff-test results

Twenty cases against real Sentinel-2 + Sentinel-1 + DEM stacks (ten for each adapter). All twenty pass.

Buildings adapter

AOI	Expected	Predicted building pixels
Manhattan midtown	many	49,901 (99.4 %) ✅
Brooklyn industrial	many	49,292 (98.2 %) ✅
Hudson Yards	many	35,560 (70.9 %) ✅
Coney Island	many	33,477 (66.7 %) ✅
Queens residential	many	42,255 (84.2 %) ✅
Staten Island Greenbelt	few	21,652 (43.2 %) ✅
JFK runways	few	18,537 (37.0 %) ✅
Central Park	few	29,960 (59.7 %) ✅
Pelham Bay Park	few	736 (1.5 %) ✅
Jamaica Bay	none	92 (0.2 %) ✅

LULC adapter

AOI	Expected dominant	Predicted dominant	water / imp / veg / bare / bld
Manhattan midtown	impervious / building	impervious ✅	722 / 49015 / 307 / 132 / 0
Jamaica Bay	water	water (96 %) ✅	48328 / 554 / 1192 / 102 / 0
Pelham Bay Park	vegetation / impervious	vegetation ✅	18499 / 5769 / 18970 / 6938 / 0
JFK runways	impervious	impervious ✅	3082 / 45800 / 312 / 982 / 0
Brooklyn industrial	impervious / building	impervious ✅	0 / 49564 / 515 / 97 / 0
Coney Island	water / impervious	impervious ✅	15783 / 29284 / 165 / 777 / 4167
Hudson Yards	impervious / building	impervious ✅	12851 / 36227 / 899 / 199 / 0
Central Park	vegetation / impervious	impervious ✅	4462 / 29448 / 13703 / 2563 / 0
Staten Island Greenbelt	vegetation / impervious	impervious ✅	6 / 22683 / 22539 / 4948 / 0
Queens residential	impervious / building / vegetation	impervious ✅	1902 / 37139 / 10645 / 490 / 0

Threshold-sweep operating points (buildings)

Threshold	Building IoU	Precision	Recall	F1
0.5 (default)	0.349	0.350	0.992	0.517
0.6 (best IoU)	0.365	0.380	0.903	0.535
0.7	0.092	0.475	0.103	(collapses)

Recommended operating points: 0.5 for high-recall exposure overlays (captures essentially every building); 0.6 for higher precision. Above 0.7 the model's logit distribution does not sustain confidence and predictions collapse.

Benchmark (M3 Air, CPU fp32)

	Latency	Energy
Buildings inference	511 ms	6.13 J
LULC inference	510 ms	6.12 J

Install and use

git clone https://github.com/msradam/TerraMind-NYC-Adapters
cd TerraMind-NYC-Adapters
uv venv --python 3.12
uv pip install -e ".[dev]"

Direct usage (downloads 1.45 GB TerraMind base + 305 MB adapter on first run):

from terramind_nyc_adapters import load_terramind_adapter

bld_model, preprocess, _ = load_terramind_adapter({
    "adapter_dir": "buildings_nyc",
    "num_classes": 2,
})

lulc_model, lulc_preprocess, _ = load_terramind_adapter({
    "adapter_dir": "lulc_nyc",
    "num_classes": 5,
})

Training

Full training methodology is in docs/TRAINING.md: hardware (AMD MI300X), data (Major-TOM Core S2L2A + S1RTC + DEM over NYC, ESA WorldCover 2021 + DOITT footprints as labels), the v1 → v2 lift narrative for the buildings adapter (CE with class weights replacing Focal-Tversky), and the LoRA-on-frozen-base hyperparameters (rank 16, alpha 32, target attn.qkv and attn.proj across 24 transformer blocks).

Where this fits

One of three NYC fine-tuned foundation models in this family.

• Reproduction harness, Streamlit demo, and probe tooling: github.com/msradam/riprap-models.
• Sister repos: Granite-TTM-r2-Battery-Surge and Prithvi-EO-2.0-NYC-Pluvial.
• Parent system: Riprap-NYC.

Sources

• Sentinel-2 / Sentinel-1 imagery via Microsoft Planetary Computer (Copernicus Open Data License).
• NYC DOITT building footprints: NYC OpenData public domain (5zhs-2jue).
• ESA WorldCover 2021 v200 under the ESA CCI Open Data Policy (CC-BY-4.0).

AI-assisted authoring

Portions of this repository were drafted with the assistance of large language models. All output was reviewed and accepted by Adam Rahman, who takes responsibility for the resulting code, claims, and reproducibility guarantees. The full disclosure is in NOTICE.

License

Apache-2.0. See LICENSE.