| 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._ |
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|
| 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. |
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|
| **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`. |
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|
| **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}, |
| } |
| ``` |
|
|
