# Detection of Large-Scale Floods Using Google Earth Engine and Google Colab
## 4 Discussion  
In this study, the use of GEE has proven to be particularly relevant and effective in addressing the needs of this research. GEE provided easy access to preprocessed SAR data and computational resources, facilitating a two-fold study. Firstly, characterising the spatiotemporal distribution of water resources in Madagascar, including seasonal and inter-annual dynamics of water bodies, and secondly, assessing the impacts of flood events resulting from cyclones in operational mode to enhance decision making using Earth observation products. In this methodology, an overall accuracy of 85% is achieved, which is similar to the accuracy attained by more advanced approaches, such as [54], using neural networks. Higher accuracy can be attained by employing more recent techniques such as automatic thresholding used by [34; 55] or deep learning used by [56], who respectively reached overall accuracies of 96.4%, 98.68% and 99.1%. Automatic thresholding requires a minimum portion of each class [42], necessitating image division into patches before thresholding. Deep learning involves deploying a model, which is a paid service on Google Earth Engine. However, the methodological developments of this flood detection tool, using Google Earth Engine and Sentinel-1 radar imagery, aim to meet operational needs during crises. This approach prioritises the speed of impact map production and precision suitable for emergency situations rather than extreme accuracy. In a crisis situation, speed is crucial, and the goal is to obtain an overview of flooded areas for effective crisis management. Thus, the methodology relies on relatively straightforward techniques. This approach strikes balances between operational speed and level precision required for informed decision making. The HAND index can play a crucial role in supporting the flood detection process [57]. We demonstrate the significance of utilising the existing global HAND dataset on GEE, emphasising its time-saving benefits in the flood detection process in addition to the accuracy improvement. The results show that several locations affected by flooding during the passage of Cyclone Batsirai were not mapped by traditional emergency services, thus demonstrating the value of this new processing chain able to systematically generate flood maps for  
Figure 7: Comparison of floods detected with processing chain and S1 Chain [49]. _Background: Google satellite._cyclone events on the scale of an entire country. 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 in space and time. New information provided by our processing chain can help to improve disaster management and response. To swiftly provide practical information to risk managers, we can enhance our analysis by integrating data from the European Space Agency (ESA) World Cover, which offers a global land cover map for 2021 at a 10-metre resolution, derived from Sentinel-1 and Sentinel-2 data. We can also utilise OpenStreetMap (OSM), a global open-source mapping platform that crowdsources geographic data, providing comprehensive, free maps for various applications. Additionally, Google Open Buildings can be used to incorporate building footprints using advanced deep learning algorithms and very high-resolution imagery. However, it is worth noting that while GEE offers data availability and processing resources, including computational power and dedicated codes, which is particularly beneficial for territories like Madagascar with vast expanses and limited resources, certain limitations need to be considered. Sentinel-1 data come preprocessed with limited flexibility for making specific adjustments. Additionally, there are restrictions on the volume of data that can be processed freely without subscribing. Ownership issues of developments can also be a concern, especially when collaborating with government agencies.