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README.md

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----
-license: cc0-1.0
----
+---
+license: cc0-1.0
+library_name: pytorch
+pipeline_tag: image-segmentation
+tags:
+  - remote-sensing
+  - earth-observation
+  - cloud-detection
+  - cloud-segmentation
+  - satellite-imagery
+  - spot-vgt
+  - proba-v
+  - semantic-segmentation
+  - pytorch
+---
+
+# ☁️ FDR4VGT-CLOUD
+
+<p align="center">
+  <img src="https://raw.githubusercontent.com/JulioContrerasH/DocProbaSpot/refs/heads/main/assets/logo_proba_spot.png" alt="FDR4VGT-CLOUD" width="90%"/>
+</p>
+
+Official model release accompanying the paper:
+
+> **A multisensor deep learning framework for robust cloud segmentation in SPOT-VGT and Proba-V**
+> Julio Contreras, Cesar Aybar, Luis Gómez-Chova
+> *IEEE Geoscience and Remote Sensing Letters*, 2026.
+
+This model is the **operational cloud masking algorithm** selected for the **ESA FDR4VGT** archive reprocessing, delivering consistent cloud detection across the full SPOT-VGT (VGT1 1998–2003, VGT2 2002–2014) and Proba-V (2013–2020) record — a single sensor-agnostic model for the three missions.
+
+---
+
+## ✨ Overview
+
+- **Architecture:** Hybrid **DeepLabV3+ (MobileNetV2 backbone) + PixelWise MLP** (`PW-DL3+`)
+- **Input:** 4 Top-of-Atmosphere reflectance bands (Blue, Red, NIR, SWIR) — sensor-agnostic
+- **Supported sensors:** SPOT-VGT1, SPOT-VGT2, Proba-V
+- **Input shape:** `[B, 4, 512, 512]`
+- **Parameters:** 12.65M (57.29 MB)
+- **Training:** Weak-to-strong supervision — large-scale pre-training on 3,647 weakly-labeled scenes, followed by fine-tuning on 109 hand-annotated hard-example scenes.
+
+---
+
+## 🚀 Quick start
+
+### Installation
+
+```bash
+pip install mlstac rasterio torch==2.5.1
+```
+
+### Inference
+
+```python
+import torch
+import mlstac
+import rasterio as rio
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 1. Load the model
+framework = mlstac.download(
+    file="https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/single/multisensor_single_1dpwdeeplabv3.json",
+    output_dir="FDR4VGT/single",
+)
+model = framework.model
+
+# 2. Load a 4-band image (Blue, Red, NIR, SWIR)
+with rio.open("https://huggingface.co/isp-uv-es/FDR4VGT-CLOUD/resolve/main/ensemble/rgb.tif") as src:
+    image = src.read()
+
+# 3. Run large-scene inference (sliding window + Hann blending)
+prob = framework.predict_large(
+    image=image,
+    model=model,
+    device=device,
+    batch_size=8,     # increase on GPU to speed up; lower on CPU
+    num_workers=8,
+    nodata=0,         # pixel value treated as invalid/padding
+)
+
+# 4. Binarize with the operational threshold
+cloud_mask = (prob.squeeze() > 0.5).astype("uint8")
+```
+
+> The binarization threshold (default `0.5`) can be tuned per use case; the paper uses the F₂-optimal threshold on the validation set.
+
+---
+
+## 📊 Performance
+
+Results on the manually-annotated test set (`PW-DL3+`, Multi-FT strategy) — mean over scenes:
+
+| Sensor    | F₂     | IoU    | κ      |
+|-----------|:------:|:------:|:------:|
+| Proba-V   | 0.891  | 0.842  | 0.808  |
+| SPOT-VGT  | 0.949  | 0.898  | 0.829  |
+
+The model substantially outperforms the legacy BS1 (physical thresholds) and BS2 (pixel-wise MLP) baselines on both sensors, with the largest gain on SPOT-VGT (ΔF₂ = +0.090 over BS1). Temporal analysis across the 1998–2020 archive shows no statistically significant discontinuity at the VGT→Proba-V transition (Mann-Whitney U, p > 0.05), in contrast to the legacy record.
+
+---
+
+## 📁 Repository layout
+
+| Path | Description |
+|------|-------------|
+| `single/multisensor_single_1dpwdeeplabv3.json` | Operational single-model weights (`PW-DL3+`) |
+| `ensemble/rgb.tif` | Example test scene (4-band TOA reflectance) |
+
+---
+
+## 📄 Citation
+
+If you use this model, please cite:
+
+```bibtex
+@article{contreras2026fdr4vgt,
+  title   = {A multisensor deep learning framework for robust cloud segmentation in SPOT-VGT and Proba-V},
+  author  = {Contreras, Julio and Aybar, Cesar and G{\'o}mez-Chova, Luis},
+  journal = {IEEE Geoscience and Remote Sensing Letters},
+  year    = {2026},
+}
+```
+
+---
+
+## 🙏 Acknowledgements
+
+This work was supported by the **European Space Agency (ESA)** within the **FDR4VGT: Fundamental Data Record for VGT** project, and by the Spanish Ministry of Science, Innovation and Universities (grant PID2023-148485OB-C21 funded by MCIU/AEI/10.13039/501100011033 ERDF, EU).
+
+Developed at the Image Processing Laboratory (IPL), University of Valencia.
+
+## 📜 License
+
+[CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/)