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giswqs/s2-water-unetplusplus-efficientnet-b4

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Surface Water Detection — EfficientNet-B4 + UNet++

A semantic segmentation model for detecting surface water from Sentinel-2 multispectral satellite imagery, trained on the Earth Surface Water Dataset.

Model Description

Property	Value
Architecture	UNet++
Encoder	EfficientNet-B4 (ImageNet pretrained)
Input	6-band Sentinel-2 (B2, B3, B4, B8, B11, B12)
Input Size	512 × 512 tiles
Output Classes	2 (Background, Water)
Parameters	~19M
Framework	segmentation-models-pytorch + PyTorch Lightning

Training Details

Parameter	Value
Dataset	Earth Surface Water Dataset
Training Tiles	1,581 (from 64 scenes, 512×512, stride 128)
Validation Tiles	396
Epochs	50
Batch Size	8
Learning Rate	1e-4
Weight Decay	1e-4
Optimizer	Adam
Loss	Cross-Entropy

Performance

Tile-level Validation (during training)

Metric	Value
Best Val IoU	0.9863
Best Val Loss	0.0112
Train IoU (final)	0.989

Full-scene Validation (31 scenes, sliding window 512×512, overlap 256)

Metric	Value
IoU	0.9635
Dice	0.9814

Usage

Installation

bash

pip install geoai-py timm segmentation-models-pytorch

Inference from HuggingFace Hub

python

import geoai

geoai.timm_segmentation_from_hub(
    input_path="sentinel2_scene.tif",
    output_path="water_mask.tif",
    repo_id="giswqs/s2-water-unetplusplus-efficientnet-b4",
    window_size=512,
    overlap=256,
    batch_size=4,
)

Input Format

The model expects 6-band Sentinel-2 L2A GeoTIFF images:

Band Index	Sentinel-2 Band	Wavelength (nm)	Resolution
1	B2 (Blue)	490	10m
2	B3 (Green)	560	10m
3	B4 (Red)	665	10m
4	B8 (NIR)	842	10m
5	B11 (SWIR1)	1610	20m
6	B12 (SWIR2)	2190	20m

Dataset

The Earth Surface Water Dataset contains 95 globally distributed Sentinel-2 scenes with binary water masks:

—Training: 64 scenes
—Validation: 31 scenes
—Source: Zenodo

Citation

bibtex

@ARTICLE{Luo2021-te,
  title     = "{An applicable and automatic method for earth surface water
               mapping based on multispectral images}",
  author    = "Luo, Xin and Tong, Xiaohua and Hu, Zhongwen",
  journal   = "International Journal of Applied Earth Observation and
               Geoinformation",
  publisher = "Elsevier BV",
  volume    =  103,
  pages     =  102472,
  year      =  2021,
  url       = "http://dx.doi.org/10.1016/j.jag.2021.102472",
  doi       = "10.1016/j.jag.2021.102472",
  issn      = "1569-8432,1872-826X",
}

License

This model is released under the CC-BY-4.0 license, consistent with the training dataset license.