| --- |
| license: mit |
| tags: |
| - remote-sensing |
| - flood-mapping |
| - segmentation |
| - earth-observation |
| - planetscope |
| pipeline_tag: image-segmentation |
| --- |
| |
| # FloodDuo |
|
|
| **A Dual Foundation-Encoder Architecture for Generalizable High-Resolution Flood Mapping** |
|
|
| Saurabh Kaushik, Beth Tellman, Qihao Weng — *ISPRS Journal of Photogrammetry and Remote Sensing*, 2026 |
| · [Code](https://github.com/Sk-2103/FloodDuo) · [Paper](https://github.com/Sk-2103/FloodDuo#citation) |
|
|
| FloodDuo maps open water in high-resolution (3 m, 4-band B/G/R/NIR PlanetScope) |
| post flood imagery by pairing two **frozen** vision foundation-model encoders that carry |
| complementary information: |
|
|
| - **DINOv3-L** (RGB) — sharp spatial / boundary detail. |
| - **Clay v1.5** (all four bands) — wavelength-conditioned spectral discrimination. |
|
|
| FloodDuo is a flood segmentation model which excel at local spatial nuances while keeping global context. It use dual encoder architechture, adapted with |
| lightweight trainable modules (an in-block frequency-domain Earth-Adapter, |
| plus post-hoc atrous-convolution and pyramid-pooling adapters), and their |
| branch predictions are merged by a **disagreement-gated fusion** whose |
| per-pixel Jensen-Shannon divergence doubles as a label-free reliability map, |
| before a LinkNet decoder produces the full-resolution mask. Only ~65M |
| parameters are trained against ~614M frozen. |
|
|
|  |
|
|
| ## Results (leave-one-region-out, region-mean water IoU) |
|
|
| | Model | FloodPlanet | UFO | |
| |---|---|---| |
| | **FloodDuo (ours)** | **0.728** | **0.819** | |
| | TerraMind-L | 0.700 | 0.789 | |
| | Prithvi-EO-2 | 0.678 | 0.774 | |
| | DOFA-L | 0.687 | 0.766 | |
| | Clay v1.5-L | 0.675 | 0.773 | |
| | CROMA-L | 0.672 | 0.715 | |
| | SSL4EO-S12 DINO | 0.650 | 0.767 | |
| | U-Net (from scratch) | 0.648 | 0.782 | |
|
|
| FloodDuo significantly outperforms U-Net (+8.0 / +3.7 pp) and the strongest |
| single-encoder foundation model (+2.8 / +3.0 pp, paired Wilcoxon on both). |
| Every table (matched single-encoder controls, the fusion ablation, |
| boundary-resolved analysis, D-reliability calibration) is in |
| [results.md](https://github.com/Sk-2103/FloodDuo/blob/main/results.md) in the |
| code repo. |
|
|
| ## This checkpoint vs. the numbers above |
|
|
| This is the **release model**: trained on all FloodPlanet + UFO data pooled |
| together (not held out by region), for actual use on new imagery. Its |
| validation IoU (0.80, on a random held-out 15% of tiles) is a different |
| measurement from the table above — that table is leave-one-region-out |
| cross-validation, a much stricter protocol specifically designed to measure |
| generalization to *unseen* regions. This release checkpoint is the practical |
| model to run on your own data, not the benchmark artifact; don't quote its |
| validation score as the paper's headline result. |
|
|
| ## Files |
|
|
| - `floodduo_full.pt` (1.36 GB) — self-contained: model config + full state |
| dict, frozen DINOv3-L and Clay v1.5 backbones included. One download, no |
| other weights needed. |
| - `examples/sample_tile.tif`, `examples/sample_mask.tif` — one held-out |
| FloodPlanet test tile (Oklahoma) for a no-setup demo, from the public |
| [FloodPlanet benchmark](https://zenodo.org/records/19698577). |
|
|
| ## Quick start |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| from src.inference import load_model, predict_file # from the GitHub repo |
| |
| ckpt = hf_hub_download("Sk-21/FloodDuo", "floodduo_full.pt") |
| model = load_model(ckpt) |
| prob, mask, profile = predict_file(model, "your_tile.tif") # 4-band B,G,R,NIR |
| ``` |
|
|
| `predict_file` auto-detects and converts scaled integer DN (e.g. Planet's own |
| Surface Reflectance product or Sentinel-2 L2A, both uint16 x10000) to |
| reflectance automatically. Full walkthrough, including a no-setup example and |
| an explicit normalization step: |
| [`notebook/inference_demo.ipynb`](https://github.com/Sk-2103/FloodDuo/blob/main/notebook/inference_demo.ipynb) |
| in the code repo. |
|
|
| ## Input format |
|
|
| 4-band (B, G, R, NIR) PlanetScope-style imagery, single tile, roughly |
| 512-1024 px per side (the size the model was evaluated at in the paper; |
| larger scenes should be tiled by the caller). Raw DN or reflectance both |
| work — see above. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{kaushik2026floodduo, |
| title = {FloodDuo: A Dual Foundation-Encoder Architecture for Generalizable High-Resolution Flood Mapping}, |
| author = {Kaushik, Saurabh and Tellman, Beth and Weng, Qihao}, |
| journal = {ISPRS Journal of Photogrammetry and Remote Sensing}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT (code and this checkpoint). The underlying FloodPlanet and UFO benchmarks |
| have their own licenses — see their Zenodo records. |
|
|
