| title | Project Specification for 2025 summer internsip |
|---|
Reconstruction of Historical Flood Extents Based on Synthetic Aperture Radar and Multi-Source Data Applications
- Build multi-sourced event-based flood extent database
:::info Integrate data from satellite, DEM, glofas, text record, etc. :::
- Explore data-driven method potential for historical flood extent reconstruction
:::info Apply ML on flood extent reconstruction :::
- Practice basic flood detection methods
- Change detection: S1 SAR
- Image classification: S2/Landsat
- Extrapolate using FLEXTH
- Event-based study for specific areas
- Hand-on practice of ML4Flood
- Literature review: state-of-the-art AI application for flood detection
- Design experiments to test the performance of different input (e.g. DEM) or model structure
- Collect flood extent data from existing dataset
- Explore data from satellite, DEM, glofas, text record, etc
- Integrate multi-sourced data for further research
- Build a viualization app with GEE app
https://docs.google.com/presentation/d/1sLopNjr3TdTDjE2u11mk9vXsgPQm07Gr2cjEPIDE3BM/edit?usp=sharing
https://drive.google.com/drive/folders/1Y34PzrjlTWpENcRUFSVVVylxa8NOC-7e?usp=sharing
https://github.com/spaceml-org/ml4floods.git
https://spaceml-org.github.io/ml4floods/content/worldfloods_dataset.html
core-five: Multi-Modal Geospatial Dataset with Perfectly Harmonized Time & Space for Foundation Models
Fakhri, F., & Gkanatsios, I. (2025). Quantitative evaluation of flood extent detection using attention U-Net case studies from Eastern South Wales Australia in March 2021 and July 2022. Scientific Reports, 15(1), 12377. https://doi.org/10.1038/s41598-025-92734-x Mateo-Garcia, G., Veitch-Michaelis, J., Purcell, C., Longepe, N., Reid, S., Anlind, A., Bruhn, F., Parr, J., & Mathieu, P. P. (2023). In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery. Scientific Reports, 13(1), 10391. https://doi.org/10.1038/s41598-023-34436-w Portalés-Julià, E., Bountos, N. I., Sdraka, M., Mateo-García, G., Papoutsis, I., & Gómez-Chova, L. (2024). Multimodal and Multitemporal Data Fusion for Flood Extent Segmentation Exploiting Kurosiwo and WorldFloods Sentinel Datasets. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 950–953. https://doi.org/10.1109/IGARSS53475.2024.10690461 Portalés-Julià, E., Mateo-García, G., Purcell, C., & Gómez-Chova, L. (2023). Global flood extent segmentation in optical satellite images. Scientific Reports, 13(1), 20316. https://doi.org/10.1038/s41598-023-47595-7 Sharma, N. K., & Saharia, M. (2025). DeepSARFlood: Rapid and automated SAR-based flood inundation mapping using vision transformer-based deep ensembles with uncertainty estimates. Science of Remote Sensing, 11, 100203. https://doi.org/10.1016/j.srs.2025.100203 Tsutsumida, N., Tanaka, T., & Sultana, N. (2025). Automated flood detection from Sentinel-1 GRD time series using Bayesian analysis for change point problems (No. arXiv:2504.19526). arXiv. https://doi.org/10.48550/arXiv.2504.19526
| Data | Type | Source | Details |
|---|---|---|---|
| Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected,log scaling | SAR C-band | GEE | 2014- |
| GPM: Global Precipitation Measurement (GPM) Release 07 | Precipitation | GEE | 2000- |
| Sentinel-2 | Multispectral | GEE | 2015-, revisit: 5 days |
| Landsat 1-5 | Multispectral | GEE | 1972-, revisit: 16 days |
| River discharge and related historical data | Discharge rate | GloFAS |
| Data | Type | Details |
|---|---|---|
| EM-DAT | .csv | |
| Global Flood Database v1 (2000-2018) | GEE | |
| ML4flood world flood database | https://spaceml-org.github.io/ml4floods/content/worldfloods_dataset.html |
Reconstruction of Historical Flood Extents Based on Synthetic Aperture Radar and Multi-Source Data Applications
- Build multi-sourced event-based flood extent database
:::info Integrate data from satellite, DEM, glofas, text record, etc. :::
- Explore data-driven method potential for historical flood extent reconstruction
:::info Apply ML on flood extent reconstruction :::
- Practice basic flood detection methods
- Change detection: S1 SAR
- Image classification: S2/Landsat
- Extrapolate using FLEXTH
- Event-based study for specific areas
- Hand-on practice of ML4Flood
- Literature review: state-of-the-art AI application for flood detection
- Design experiments to test the performance of different input (e.g. DEM) or model structure
- Collect flood extent data from existing dataset
- Explore data from satellite, DEM, glofas, text record, etc
- Integrate multi-sourced data for further research
- Build a viualization app with GEE app
https://docs.google.com/presentation/d/1sLopNjr3TdTDjE2u11mk9vXsgPQm07Gr2cjEPIDE3BM/edit?usp=sharing
https://drive.google.com/drive/folders/1Y34PzrjlTWpENcRUFSVVVylxa8NOC-7e?usp=sharing
https://github.com/spaceml-org/ml4floods.git
https://spaceml-org.github.io/ml4floods/content/worldfloods_dataset.html
core-five: Multi-Modal Geospatial Dataset with Perfectly Harmonized Time & Space for Foundation Models
Fakhri, F., & Gkanatsios, I. (2025). Quantitative evaluation of flood extent detection using attention U-Net case studies from Eastern South Wales Australia in March 2021 and July 2022. Scientific Reports, 15(1), 12377. https://doi.org/10.1038/s41598-025-92734-x Mateo-Garcia, G., Veitch-Michaelis, J., Purcell, C., Longepe, N., Reid, S., Anlind, A., Bruhn, F., Parr, J., & Mathieu, P. P. (2023). In-orbit demonstration of a re-trainable machine learning payload for processing optical imagery. Scientific Reports, 13(1), 10391. https://doi.org/10.1038/s41598-023-34436-w Portalés-Julià, E., Bountos, N. I., Sdraka, M., Mateo-García, G., Papoutsis, I., & Gómez-Chova, L. (2024). Multimodal and Multitemporal Data Fusion for Flood Extent Segmentation Exploiting Kurosiwo and WorldFloods Sentinel Datasets. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 950–953. https://doi.org/10.1109/IGARSS53475.2024.10690461 Portalés-Julià, E., Mateo-García, G., Purcell, C., & Gómez-Chova, L. (2023). Global flood extent segmentation in optical satellite images. Scientific Reports, 13(1), 20316. https://doi.org/10.1038/s41598-023-47595-7 Sharma, N. K., & Saharia, M. (2025). DeepSARFlood: Rapid and automated SAR-based flood inundation mapping using vision transformer-based deep ensembles with uncertainty estimates. Science of Remote Sensing, 11, 100203. https://doi.org/10.1016/j.srs.2025.100203 Tsutsumida, N., Tanaka, T., & Sultana, N. (2025). Automated flood detection from Sentinel-1 GRD time series using Bayesian analysis for change point problems (No. arXiv:2504.19526). arXiv. https://doi.org/10.48550/arXiv.2504.19526
| Data | Type | Source | Details |
|---|---|---|---|
| Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar Ground Range Detected,log scaling | SAR C-band | GEE | 2014- |
| GPM: Global Precipitation Measurement (GPM) Release 07 | Precipitation | GEE | 2000- |
| Sentinel-2 | Multispectral | GEE | 2015-, revisit: 5 days |
| Landsat 1-5 | Multispectral | GEE | 1972-, revisit: 16 days |
| River discharge and related historical data | Discharge rate | GloFAS |
| Data | Type | Details |
|---|---|---|
| EM-DAT | .csv | |
| Global Flood Database v1 (2000-2018) | GEE | |
| ML4flood world flood database | https://spaceml-org.github.io/ml4floods/content/worldfloods_dataset.html |