---
license: cc-by-nc-4.0
tags:
  - remote-sensing
  - segmentation
  - field-boundary-delineation
  - agriculture
  - planetscope
  - pytorch-lightning
  - onnx
  - semantic-segmentation
library_name: pytorch
pipeline_tag: image-segmentation
---

# FTP-PRUE+ (EfficientNet-B3) — Fields of the Planet

3m PlanetScope field-boundary segmentation model from **Fields of the Planet
(FTP)**, a PlanetScope companion to [Fields of the World v2
(FTW)](https://github.com/fieldsoftheworld/ftw-baselines). This is the
paper's main headline model.

U-Net decoder over an EfficientNet-B3 encoder, trained on paired early- and
peak-season PlanetScope surface-reflectance imagery (8 input channels: 2
seasonal windows x 4 bands) to predict a 3-class field mask (background /
field / boundary) at 3m ground sample distance.

- **Repo:** [taylor-geospatial/ftw-planet](https://github.com/taylor-geospatial/ftw-planet)
- **Paper:** Fields of the Planet: Field Boundary Mapping Beyond 10m
- **Architecture:** `smp.Unet(encoder_name="efficientnet-b3")`, `in_channels=8`, `classes=3`
- **Training resolution:** 512x512 crops, 3m/px
- **Loss:** log-cosh Dice, class weights `[0.05, 0.2, 0.75]`, `ignore_index=3`
- **Recipe:** PRUE+ (geometry/noise augmentations, watershed post-processing, D4 TTA at eval)

## Files

| File | Format | Notes |
|---|---|---|
| `config.json` + `model.safetensors` | `segmentation_models_pytorch` Hub format | recommended for most users — safetensors (no pickle), self-describing architecture, needs only `pip install segmentation-models-pytorch` |
| `ftp-b3.ckpt` | PyTorch Lightning checkpoint | `state_dict` + `hyper_parameters` only — optimizer/scheduler state stripped |
| `ftp-b3.onnx` | ONNX (opset 17) | single-file, dynamic batch/H/W, needs `onnxruntime` (plain PyTorch cannot execute `.onnx`) |
| `ftp-b3.pt2` | `torch.export` ExportedProgram | standalone, dynamic batch / static 512x512, loads with `torch.export.load` — no `ftw_planet` install needed |

## Usage

### segmentation_models_pytorch (recommended)

```python
import segmentation_models_pytorch as smp

model = smp.from_pretrained("taylor-geospatial/ftp-b3").eval()
logits = model(image)  # image: (B, 8, H, W) float32, 2 seasonal windows x 4 bands
```

### Lightning checkpoint (raw `smp.Unet`)

The checkpoint's `state_dict` is just an `smp.Unet` under a `model.` prefix —
unless you need the Lightning training wrapper, `smp.from_pretrained` above
is simpler.

```python
import torch
import segmentation_models_pytorch as smp

ckpt = torch.load("ftp-b3.ckpt", map_location="cpu")
hp = ckpt["hyper_parameters"]
model = smp.Unet(encoder_name=hp["backbone"], encoder_weights=None, in_channels=hp["in_channels"], classes=hp["num_classes"])
model.load_state_dict({k.removeprefix("model."): v for k, v in ckpt["state_dict"].items()})
model.eval()

logits = model(image)  # image: (B, 8, H, W) float, 2 seasonal windows x 4 bands
```

### ONNX

```python
import onnxruntime as ort

sess = ort.InferenceSession("ftp-b3.onnx", providers=["CPUExecutionProvider"])
logits = sess.run(None, {"image": image_np})[0]  # image_np: (B, 8, H, W) float32
```

### torch.export (standalone, no `ftw_planet` needed)

```python
import torch

exported = torch.export.load("ftp-b3.pt2")
model = exported.module()
logits = model(image)  # image: (B, 8, 512, 512) float32, any batch size
```

Output is 3-class logits (background / field / boundary); argmax + the
repo's watershed post-processing (`scripts/eval/postprocess_eval.py`)
recovers instance polygons.

## Results

Macro-averaged over the 10 dense held-out FTW-v2 countries, true-polygon
scoring at native 3m GSD (see paper Table 1): PQ 35.5, sub-0.5ha PQ 15.7,
matched-boundary error 7.4m.

## Citation

```bibtex
@misc{ftw-planet,
  author = {Corley, Isaac and Robinson, Caleb and Marcus, Jennifer and Kerner, Hannah},
  title  = {Fields of the Planet: Field Boundary Mapping Beyond 10m},
  year   = {2026},
  url    = {https://github.com/taylor-geospatial/ftw-planet}
}
```

## License

Weights: CC-BY-NC-4.0. Trained on PlanetScope imagery (c) Planet Labs PBC,
exported under the NICFI / research program, and FTW v2 polygons (CC-BY-4.0).
Refer to those source terms for any redistribution of the underlying data.