Dataset for domain shift test in Sen1Floods11 training set, this dataset is manually curated for flood and cloud occlusions test set from WorldFloodsv2 sourced from Pleiades-1A/1B (1), PlanetScope (3) and Sentinel-2 (1).
Based on the paper **Cross-Resolution Domain Shift: From U-Net Encoder–Decoder to the Prithvi Hybrid Model** _Gunawan, A. A. S., Kam, R. M., & Andrew, H._ 

Each samples varies in HxW pixel count, refering to `process_patches.py / sliding_window_crop()`. Given an image tensor of shape `(C, H, W)`, the function scans across the spatial dimensions in steps of `stride=224`, extracting a `224×224` window at each position.

**Edge handling** : When a window would extend beyond the image boundary, the endpoint is clamped to the image edge (`y_end = min(y + 224, H)`), and the start is *recalculated backwards* from that clamped end (`y_start = max(y_end - 224, 0)`). This means the last patch in any row or column overlaps with the second-to-last patch rather than being smaller, every patch is guaranteed to be exactly `224×224`.

**Reproducibility** : Each AOI (Area of interest) were hand-picked to give the best possible analysis of flood segmentation and cloud robustness, refer to `EMSR507_[AOI] / include.txt` that includes the geotiff suffix for testing set. 

---

## Sentinel-2 — AOI05

Follows the standard **WorldFloods 12-band format**, resampled to 10–20 m resolution. The SWIR bands (B11, B12) are the most critical for flood mapping — water strongly absorbs SWIR energy, making them the primary tool for separating open water from cloud shadows and dark surfaces.

| Band | Name | Resolution |
|------|------|------------|
| B1 | Ultra Blue | 60 m |
| B2 | Blue | 10 m |
| B3 | Green | 10 m |
| B4 | Red | 10 m |
| B5 | Red Edge (VRE 1) | 20 m |
| B6 | Red Edge (VRE 2) | 20 m |
| B7 | Red Edge (VRE 3) | 20 m |
| B8 | NIR | 10 m |
| B8A | Narrow NIR | 20 m |
| B9 | Water Vapour | 60 m |
| B11 | SWIR 1 | 20 m |
| B12 | SWIR 2 | 20 m |

---

## PlanetScope — AOI02, AOI03, AOI07

A compact **4-band** sensor optimized for high temporal frequency and sharp spatial detail. Without SWIR coverage, water-shadow discrimination is more limited compared to Sentinel-2.

| Band | Name | Resolution |
|------|------|------------|
| Band 1 | Blue | 3 m |
| Band 2 | Green | 3 m |
| Band 3 | Red | 3 m |
| Band 4 | Near-Infrared (NIR) | 3 m |

---

## Pleiades-1A / 1B — AOI01

A **4-band** sensor delivering very high spatial resolution, frequently pan-sharpened to 0.5 m. Fine-grained features like building footprints and narrow waterways become resolvable, though no SWIR bands are available.

| Band | Name | Resolution |
|------|------|------------|
| Band 1 | Blue | 2 m (0.5 m pan-sharpened) |
| Band 2 | Green | 2 m (0.5 m pan-sharpened) |
| Band 3 | Red | 2 m (0.5 m pan-sharpened) |
| Band 4 | Near-Infrared (NIR) | 2 m (0.5 m pan-sharpened) |

---


### Citations

```bibtex
@article{portales-julia_global_2023,
	title = {Global flood extent segmentation in optical satellite images},
	volume = {13},
	issn = {2045-2322},
	doi = {10.1038/s41598-023-47595-7},
	number = {1},
	urldate = {2023-11-30},
	journal = {Scientific Reports},
	author = {Portalés-Julià, Enrique and Mateo-García, Gonzalo and Purcell, Cormac and Gómez-Chova, Luis},
	month = nov,
	year = {2023},
	pages = {20316},
}
@article{mateo-garcia_inorbit_2023,
	title = {In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery},
	volume = {13},
	issn = {2045-2322},
	url = {https://www.nature.com/articles/s41598-023-34436-w},
	doi = {10.1038/s41598-023-34436-w},
	number = {1},
	urldate = {2023-06-27},
	journal = {Scientific Reports},
	author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Josh and Purcell, Cormac and Longepe, Nicolas and Reid, Simon and Anlind, Alice and Bruhn, Fredrik and Parr, James and Mathieu, Pierre Philippe},
	month = jun,
	year = {2023},
	pages = {10391},
}

@article{mateo-garcia_towards_2021,
	title = {Towards global flood mapping onboard low cost satellites with machine learning},
	volume = {11},
	issn = {2045-2322},
	doi = {10.1038/s41598-021-86650-z},
	number = {1},
	urldate = {2021-04-01},
	journal = {Scientific Reports},
	author = {Mateo-Garcia, Gonzalo and Veitch-Michaelis, Joshua and Smith, Lewis and Oprea, Silviu Vlad and Schumann, Guy and Gal, Yarin and Baydin, Atılım Güneş and Backes, Dietmar},
	month = mar,
	year = {2021},
	pages = {7249},
}
```