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1efb8c9 9923d44 07aafca 47ca7c7 1a71d19 e24a1f4 b5c766c 509d3e7 e24a1f4 07aafca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | 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},
}
```
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