---
license: cc-by-4.0
pretty_name: GeoPathfinder
task_categories:
- image-classification
tags:
- remote-sensing
- earth-observation
- landsat
- long-range-reasoning
- benchmark
- geospatial
size_categories:
- 10K<n<100K
---

# GeoPathfinder

A Pathfinder-style long-range spatial reasoning benchmark on real satellite
imagery. Each sample is a 256×256 Landsat patch (native 30 m resolution, a
7.7×7.7 km footprint) with two red dots on land; the task is to predict whether
the dots are connected by land (`connect = 1`) or separated by water
(`disconnect = 0`). Labels derive from Overture Maps water polygons rasterized
onto the exact pixel grid of each patch and cross-checked against Landsat's own
QA water flag.

- **5,273 patches → 10,546 images** across **42 river basins on six
  continents**, exactly class-balanced (each patch yields one connect and one
  disconnect sample, distance-matched within 25% so dot separation carries no
  label signal)
- **Adversarial dot placement**: each released pair is the most deceptive of a
  large candidate pool (96 for train/val, 256 for test), deterministic given
  the masks — randomly placed dots admit a sight-line shortcut
- Fixed 80/10/10 patch-level splits (paired images never straddle a split).
  A basin-disjoint protocol
  (`splits_basin.json`, repo root) holds out four basins on four continents
  for geographic-generalization tests
- `eval_variants/` ships a fixed easy/medium/hard placement suite (1,051
  images per tier): report per-tier accuracy and the macro average
  (placement-general score) alongside test accuracy
- Code, construction pipeline, baselines:
  https://github.com/isaaccorley/geo-long-range-arena

## Download

The full dataset ships as a single archive that extracts to a `geopathfinder/`
folder:

```bash
hf download isaaccorley/GeoPathfinder geopathfinder.tar.gz --repo-type dataset --local-dir .
tar -xzf geopathfinder.tar.gz
```

`metadata.parquet` and `water_polygons.parquet` are also available uncompressed
at the repo root for querying without downloading the archive.

## Files (inside geopathfinder.tar.gz)

```
pathfinder/connect/     patch_XXXXX.jpg    benchmark image, label = 1
pathfinder/disconnect/  patch_XXXXX.jpg    benchmark image, label = 0
images/                 patch_XXXXX.jpg    true color without dots (pristine)
raw/                    patch_XXXXX.tif    uint16 RGBN surface-reflectance DNs,
                                           scale/offset in band metadata, scene
                                           provenance in file tags (ZSTD)
masks/                  patch_XXXXX.png    0=land 1=water 2=invalid 3=suspect
overlays/               patch_XXXXX.jpg    water mask blended over true color (QC)
metadata.parquet                           one row per patch: split, region, scene
                                           id, bounds, CRS/transform, QC stats,
                                           dot pixel + lon/lat coordinates
water_polygons.parquet                     281k Overture water polygons clipped to
                                           patch bounds (GeoParquet, WGS84), keyed
                                           by sample_id; geometry_patch_crs holds
                                           the same shapes as WKB in each patch's
                                           UTM CRS (patch_crs column)
splits.json                                train/val/test patch ids
plan.json                                  build configuration snapshot
```

Rasterizing a patch's rows from `water_polygons.parquet` onto its
`transform`/`crs` (in `metadata.parquet`) reproduces its stored mask
(IoU ≥ 0.999).

## Quick start

```python
import pandas as pd

meta = pd.read_parquet("metadata.parquet")
train = meta[meta.split == "train"]
# each labeled patch contributes two images:
#   pathfinder/connect/{sample_id}.jpg    label 1
#   pathfinder/disconnect/{sample_id}.jpg label 0
```

Raw reflectance (for geospatial foundation models — dots are not burned into
these; use the dot coordinates from `metadata.parquet`):

```python
import rasterio

with rasterio.open("raw/patch_00122.tif") as src:
    reflectance = src.read().astype("float32") * src.scales[0] + src.offsets[0]
```

## Baselines

Two numbers per model: accuracy on the released test split, and the
**placement-general score** (macro accuracy over the easy/medium/hard suite,
seed-42 checkpoints). Accuracies are mean ± std over three seeds.

Reference pipelines and probes:

| approach | test acc | macro |
|---|---|---|
| Water index (blue-red on RGB), no learning | 52.8% | 54.1 |
| Water index (NDWI on green/NIR), no learning | 69.9% | 76.2 |
| U-Net masks -> connected components | 69.2 ± 0.6% | 78.2 ± 1.1 |
| Best frozen linear probe (13 pretrainings) | 62.7% | — |

Architectures (fine-tuned on the released placements, three-seed means):

| model | test acc | macro |
|---|---|---|
| MambaVision-B (ext. recipe) | **88.0 ± 0.6%** | **62.7 ± 0.7** |
| OverLoCK-B | **88.0 ± 0.8%** | 62.1 ± 1.2 |
| FocalNet-B LRF | 87.8 ± 0.4% | 62.2 ± 1.1 |
| MaxViT-B | 87.5 ± 0.9% | 61.0 ± 4.4 |
| ConvNeXt-B | 87.2 ± 0.9% | 61.0 ± 0.5 |
| FocalNet-B SRF | 86.8 ± 0.6% | 61.0 ± 0.9 |
| UniRepLKNet-T | 86.6 ± 1.0% | 60.7 ± 0.9 |
| RepLKNet-31B (ext. recipe) | 86.4 ± 0.8% | 60.7 ± 0.6 |
| MambaOut-B | 86.4 ± 0.3% | 62.0 ± 1.5 |
| DaViT-B (2/3 seeds converge) | 86.4 ± 0.5% | 59.9 ± 1.3 |
| ResNet-50 | 85.3 ± 0.6% | 59.0 ± 1.0 |
| Swin-B | 83.7 ± 1.3% | 58.5 ± 2.2 |
| ViT-B/16 (ext. recipe) | 82.0 ± 1.0% | 56.4 ± 1.2 |
| ResNet-18 (scratch) | 78.0 ± 0.4% | 53.2 ± 1.1 |

All fourteen architectures land between 53.2 and 62.7 macro (a further 22
size/pretraining/weight variants stay inside 58-65). Every model trained on
the released placements collapses on the macro score (models learn "the
obvious answer is wrong" and fail easy placements); retraining with dots
resampled every epoch recovers up to 85.1 ± 0.3 macro (MaxViT) while the
hard tier stays around 75% — the benchmark is far from solved. Marker shape
and color barely matter (±2 points). Full protocol and training recipes:
https://github.com/isaaccorley/geo-long-range-arena

## Provenance and licensing

- Imagery: Landsat Collection 2 Level-2 (USGS, public domain), accessed via the
  Microsoft Planetary Computer. Scene ids, WRS path/row, and acquisition times
  are embedded in each GeoTIFF and in `metadata.parquet`.
- Water geometry: Overture Maps `base/water` (release 2026-06-17.0), which
  includes OSM-derived data © OpenStreetMap contributors. The
  `water_polygons.parquet` and `masks/` layers are therefore available under
  ODbL; annotations and imagery composites are CC-BY-4.0.

## Citation

```bibtex
@misc{corley2026geopathfinder,
  title  = {GeoPathfinder: Long-Range Spatial Reasoning in Satellite Imagery},
  author = {Corley, Isaac},
  year   = {2026},
  url    = {https://huggingface.co/datasets/isaaccorley/GeoPathfinder}
}
```