Description

Sentinel-1 satellite Synthetic Aperture Radar (SAR) data have successfully shown its potential for natural and human-induced hazards monitoring since 2014. As Sentinel-1 SAR satellite routinely offers free and large-scale SAR images covering almost entire world on a weekly basis, the pre- and post-hazard, and even co-hazard analysis, at any location and any time, is possible to be conducted. This study will demonstrate the potential of pre- and post-landslide analysis using Sentinel-1 SAR data with a designated 2019 landslide in Pokot, Northwest Kenya. This 2019 Kenya landslide occurred 23 November 2019 due to consecutive and heavy rainfall, and led to 29 people killed and dozens of more people injured.

Objectives and Methodology

This study will make use of labelled UAV / satellite imagery across different urban environments. A supervised machine learning method (e.g. Fully Convolutional Networks) will be trained on one study area, and the generalization of that model will be assessed on different study areas. Improvements will be made to improve the transparency of the results to give end-users a better idea of the accuracy of the predictions. Additionally, active learning methods will be employed to indicate which samples should next be labelled in order to improve the results when generalized to a different domain.

Further reading

Ferretti, A., Prati, C., and Rocca, F. (2001). Permanent scatterers in SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 39(1):8–20.
Berardino, P., Fornaro, G., Lanari, R., and Sansosti, E. (2002). A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms. IEEE Transactions on Geoscience and Remote Sensing, 40(11):2375–2383.