2023-03-06, 11:45–12:15, 01.S.21 (1st floor)
This talk will present FLORIA, an innovative custom python pipeline for urban flood extraction. The implementation of the predefined workflow, ingesting most SAR sensors and extracting from U-Net convolutional network approach, will be highlighted. Thanks to open-source data, software and libraries, mapping and automatizing urban flood extraction from SAR imagery is now possible.
Mapping flood in urban areas with SAR imagery is not possible from a single imagery due to the dihedral backscattering effect of man-made structures. Scientific papers have shown that the backscattered double bounce of the signal is amplified by the water presence and the coherence between pairs of SAR - that is expected to be high in an urban area - is falling down.
This assumption, accompanied by several tests, made us lead to build a Python library called SarPair. It is an interferometric SAR oriented module, based on EOReader to simplify the data ingestion and SNAP for the SAR processing. On the other hand, we developed another Python module running U-Net convolutional network based on Tensorflow.
Both modules have then been linked together in a Docker to get a full pipeline from SAR images to the urban flood extraction. At the end we can now, thanks to open source SAR data and libraries, monitor any urban flood in the world with a simple click.