HydroFlow: 12-Channel Sentinel-2 Multispectral Water Segmentation Model

HydroFlow is a production-grade deep learning model for high-resolution surface water extraction from 12-band Sentinel-2 multispectral satellite observations. Based on a transfer-learning U-Net architecture with an ImageNet-pretrained ResNet-34 backbone adapted for 12 spectral input channels, HydroFlow achieves 81.66% Global Pixel IoU (and 82.10% Peak Validation IoU) on strictly unseen satellite scenes.

Verified Project Resources & Official Links

Resource	Access URL	Description
🌐 Interactive Web Platform	Launch Platform	Hosted on GitHub Pages: Earth Explorer, Satellite Raster Upload, and In-Browser AI Engine
📊 Research Benchmark Analytics	View Dashboard	Standalone interactive dashboard: dynamic loss curves, Pareto efficiency frontier, NDWI calculator
⚡ Cloud Inference Engine	Render API Service	Hosted on Render: High-performance ONNX Runtime inference server (`/predict`, `/predict_geo`)
💻 GitHub Repository	Source Code	Complete reproducible research codebase, modular pipelines, training CLI, and tests
📦 Hugging Face Model Hub	Model Weights	Official PyTorch state dicts, standard ONNX graph, and INT8 quantized edge models
📓 Kaggle Research Collection	Explore 6 Notebooks	Full scientific reproducibility suite (EDA, Scratch, Ablation, Flow Matching, Transfer Learning)

Model Checkpoint Files

File Name	Format	Size	Target Environment & Description
`best_model_quant.onnx`	ONNX (INT8)	24.6 MB	Edge & Browser Production Runtime: Dynamic INT8 quantized ONNX graph. 44ms CPU latency, 0.03% IoU drop, 75% memory savings.
`best_model.onnx`	ONNX (FP32)	97.8 MB	Standard Server Production: Full-precision dynamic-batch ONNX graph for ONNX Runtime (CPU/CUDA/TensorRT).
`best_model.pth`	PyTorch State Dict	98.0 MB	PyTorch Fine-Tuning: Raw PyTorch weights compatible with `segmentation-models-pytorch`.
`config.json`	JSON	1.2 KB	Architectural configuration, Sentinel-2 band mappings, and physical calibration min-max constants.
`samples/`	GeoTIFF & PNG	~3.2 MB	8 diverse, verified validation scenes with ground truths, RGB composites, and overlays.

Official Master 6-Way Comparative Benchmark

All experiments evaluated under strict scientific parity on 62 strictly unseen validation scenes (1,015,808 pixels):

Experiment ID	Architecture & Paradigm	Backbone	Input Bands	Params	Global Pixel IoU	Precision	Recall	F1-Score
`W1-Scratch-12ch`	`Custom U-Net (Scratch)`	Scratch Baseline	12 Bands	7.77M	`73.33%`	`86.85%`	`82.50%`	`84.62%`
`W1-Scratch-6ch`	`Custom U-Net (Scratch)`	Scratch Baseline	6 Golden Bands	7.76M	`64.92%`	`84.81%`	`73.47%`	`78.73%`
`W2-Pretrained-ResNet34-12ch`	`SMP U-Net (Transfer)`	ResNet-34	12 Bands	24.46M	`81.66%`	`91.48%`	`88.39%`	`89.91%`
`W2-Pretrained-ResNet34-6ch`	`SMP U-Net (Transfer)`	ResNet-34	6 Golden Bands	24.46M	`79.80%`	`91.83%`	`85.89%`	`88.75%`
`W2-Pretrained-EffNetB0-12ch`	`SMP U-Net (Transfer)`	EfficientNet-B0	12 Bands	6.25M	`76.56%`	`86.73%`	`86.72%`	`86.72%`
`W2-Pretrained-EffNetB0-6ch`	`SMP U-Net (Transfer)`	EfficientNet-B0	6 Golden Bands	6.25M	`74.82%`	`83.61%`	`87.68%`	`85.60%`

Prepackaged Diverse Validation Suite (8 Hydrological Regimes)

Included directly in samples/ for rapid reproducibility:

Scene ID	Hydrological Category	Physical Regime Description	GT Water	Pred Water	Validation IoU
`sample_1_lake`	Inland Lake / Reservoir	Expansive open reservoir with sharp littoral boundaries	`76.90%`	`78.26%`	`93.94%`
`sample_2_river`	Meandering River & Shoreline	High-curvature river channel with vegetative bank transitions	`35.28%`	`34.38%`	`96.15%`
`sample_3_stream`	Narrow Streams & Wetland	Fine marshland tributaries near the 10m spatial resolution limit	`8.02%`	`9.13%`	`87.21%`
`sample_4_deepwater`	Open Deep Water Basin	Vast open water body with deep optical absorption & low noise	`89.38%`	`89.29%`	`98.26%`
`sample_5_estuary`	Coastal Bay & Estuary	Dynamic tidal flats, shallow lagoons, and brackish shorelines	`57.26%`	`57.72%`	`96.14%`
`sample_6_oasis`	Arid Reservoir & Oasis	Desert reservoir with high-contrast dry sand and rocky topography	`24.70%`	`24.87%`	`89.99%`
`sample_7_canal`	Agricultural Canal Network	Geometric irrigation channels intersecting cultivated cropland	`17.61%`	`16.37%`	`90.55%`
`sample_8_creek`	Thin Creek & Riparian Tributary	Sub-kilometer meandering stream through dense canopy & alluvium	`3.89%`	`4.04%`	`83.99%`

Quickstart: Python Inference (3 Lines)

Using ONNX Runtime (Ultra-Fast CPU/GPU)

import onnxruntime as ort
import tifffile as tiff
import numpy as np

# 1. Initialize ONNX Runtime session
session = ort.InferenceSession("best_model_quant.onnx", providers=["CPUExecutionProvider"])

# 2. Load and calibrate Sentinel-2 12-band raster
band_mins = np.array([69.0, 100.0, 233.0, 104.0, 127.0, 14.0, 11.0, 64.0, 17.0, 17.0, 10.0, 0.0], dtype=np.float32)
band_maxs = np.array([941.0, 1143.0, 1733.0, 2478.0, 4046.0, 4453.0, 3761.0, 224.0, 1902.0, 1907.0, 80.0, 98.0], dtype=np.float32)

raw = tiff.imread("samples/sample_1_lake.tif").astype(np.float32)
norm = np.clip((raw.transpose(2, 0, 1) - band_mins[:, None, None]) / (band_maxs[:, None, None] - band_mins[:, None, None] + 1e-6), 0.0, 1.0)

# 3. Predict binary water mask (< 25 ms)
logits = session.run(None, {"input": norm[np.newaxis, ...]})[0]
water_mask = (1.0 / (1.0 + np.exp(-logits[0, 0]))) >= 0.50
print(f"Surface water pixels: {water_mask.sum()} / {water_mask.size} ({water_mask.mean()*100:.2f}%)")

Citation & Attribution

@misc{elbasyouni2026hydroflow,
  author = {Mohamed Mostafa Elbasyouni},
  title = {HydroFlow: Multispectral 12-Channel Satellite Water Segmentation, Optimal Transport Flow Matching, and Transfer Learning},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/egyx/hydroflow-water-segmentation}}
}