# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 5 Conclusions  
This study developed a processing chain to identify flooded areas using radar imagery in Google Earth Engine. The chain was developed in Google Colab in the form of a Python notebook. We have shown that our processing chain is effective in detecting flooded areas throughout Madagascar. This processing over large areas was possible thanks to the computing power and the rapid access to the data offered by Google Earth Engine. The GEE platform is a real opportunity for work on large volumes of data, particularly for countries like Madagascar where bandwidth and the Internet network can be quite inconsistent. The results show that several locations affected by flooding during the passage of Cyclone Batsirai were not mapped by traditional emergency mapping services, thus demonstrating the value of systematically generating national flood maps for cyclone events. The maps used by national emergency management agencies, obtained from various sources including through activation of the International Charter Space and Major Disasters, currently remain very limited. New information provided by our processing chain can help to improve disaster management and response. Cyclonic seasons give rise to clouds and atmospheric disturbances. Less affected than optical imagery, we have shown that radar Sentinel-1 data are a reliable solution for detecting flooding during these periods. It makes sense to continue working on these data, since ESA has announced the launch of Sentinel-1-C this year, and the rest of the programme is already planned. Building processing chains and operational tools based on Sentinel-1 data is part of a long-term vision. Code from the processing chain could easily be adapted to use other radar images from different satellite constellations and to complete or replace Sentinel-1 imagery when it is not available. The use of several sources of radar images in parallel could even make it possible to improve the overall precision of the method, through a greater temporal density of images, making it possible, for example, to capture critical periods for detecting the peak of flooding (a few hours to few days after the passage of the cyclones). Cross-referencing data from various sources (different sensors, trajectories or angles of incidence) could also make it possible to refine the algorithms to complete flooded areas, or mask noise and poor detections. As shown by the validation of the results with Sentinel-2 data, the use of optical images could also be considered in addition, especially since the number of constellations of Earth observation satellites of this type is constantly increasing (Planet, etc.). The algorithms will also need to be adapted to process these types of data. The chain currently runs on Google Earth Engine. Adapting the script to work with multiple backends such as Digital Earth Africa [58] or Microsoft Planetary Computer [59] or other proposals like the ESA's [60] would be very interesting. The use of these different platforms could also provide access to different data sources important for rapid detection of all flooded areas. The good results obtained for the detection of areas under water and the replicable nature of the method raise the question of its use at an operational level. To this end, we have launched the SCO (Space Climate Observatory) Cimopolee project, a research and operational application project involving our teams and the Bureau National de Gestion des Risques et des Catastrophes a Madagascar (BNGRC). The aim of this project is to continue improving the results of the processing chain, but above all, to integrate the results and provide operational information for decision makers and people in charge of risk management in Madagascar. Conceptualisation, R.J., C.R. and S.R. (Solofo Rakotomdarompiana); methodology, R.J., T.C., S.R. (Solofoarisoa Rakotomiaina) and S.R. (Solofo Rakotomdarompiana); software, R.J., T.C. and P.M.; validation, R.J., C.R. and T.C.