https://github.com/msradam/terramind-nyc-adapters

TerraMind 1.0 LoRA adapter family for NYC: building-footprint segmentation, 5-class land cover, and TiM. Multi-modal Sentinel-2 + Sentinel-1 + DEM. Apache-2.0.
https://github.com/msradam/terramind-nyc-adapters

apache-2 building-segmentation foundation-models land-cover lora nyc peft sentinel-1 sentinel-2 terramind

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TerraMind 1.0 LoRA adapter family for NYC: building-footprint segmentation, 5-class land cover, and TiM. Multi-modal Sentinel-2 + Sentinel-1 + DEM. Apache-2.0.

Host: GitHub
URL: https://github.com/msradam/terramind-nyc-adapters
Owner: msradam
License: apache-2.0
Created: 2026-05-10T14:39:02.000Z (2 months ago)
Default Branch: main
Last Pushed: 2026-05-10T15:37:05.000Z (2 months ago)
Last Synced: 2026-05-10T16:34:14.731Z (2 months ago)
Topics: apache-2, building-segmentation, foundation-models, land-cover, lora, nyc, peft, sentinel-1, sentinel-2, terramind
Language: Python
Homepage: https://huggingface.co/msradam/TerraMind-NYC-Adapters
Size: 2.73 MB
Stars: 1
Watchers: 0
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE
- Notice: NOTICE

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README

          # TerraMind-NYC-Adapters

LoRA adapters that specialise [`ibm-esa-geospatial/TerraMind-1.0-base`](https://huggingface.co/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`](https://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:

![Manhattan midtown buildings](assets/terramind_buildings_manhattan_midtown.png)

Jamaica Bay — model correctly finds 0.18 % buildings:

![Jamaica Bay buildings](assets/terramind_buildings_jamaica_bay.png)

Central Park — mixed urban / vegetation:

![Central Park buildings](assets/terramind_buildings_central_park.png)

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

Manhattan midtown — dominantly impervious:

![Manhattan midtown LULC](assets/terramind_lulc_manhattan_midtown.png)

Jamaica Bay — 96 % water:

![Jamaica Bay LULC](assets/terramind_lulc_jamaica_bay.png)

Central Park — vegetation visible:

![Central Park LULC](assets/terramind_lulc_central_park.png)

## 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

```bash

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):

```python

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`](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](https://github.com/msradam/riprap-models).

- **Sister repos:**

  [Granite-TTM-r2-Battery-Surge](https://github.com/msradam/Granite-TTM-r2-Battery-Surge) and

  [Prithvi-EO-2.0-NYC-Pluvial](https://github.com/msradam/Prithvi-EO-2.0-NYC-Pluvial).

- **Parent system:** [Riprap-NYC](https://github.com/msradam/riprap-nyc).

## Sources

- Sentinel-2 / Sentinel-1 imagery via

  [Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/)

  (Copernicus Open Data License).

- NYC DOITT building footprints: NYC OpenData public domain

  ([`5zhs-2jue`](https://data.cityofnewyork.us/Housing-Development/Building-Footprints/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`](NOTICE).

## License

Apache-2.0. See [`LICENSE`](LICENSE).

ecosyste.ms

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