# Post-Disaster Recovery Monitoring with Google Earth Engine
## 4 Discussion and Conclusions  
The aim of this study was to assess the suitability of GEE for post-disaster recovery assessment for a large-scale region after Typhoon Haiyan, which hit Leyte island in the Philippines in 2013. We proposed an approach to monitor and quantify the recovery process using GEE, in particular with Landsat images, based on land cover classification and subsequent change analysis, and then producing municipality-based damage and recovery maps. The land cover classification results show the robustness of the employed approach, including image composite generation and Random Forest-based image classification. Furthermore, the change analysis and generated recovery maps clearly illustrate the damage and recovery processes during the selected time epochs, i.e., three time-steps for recovery assessment for three years after Haiyan. However, among the big geo dataset available in the platform and considering the time of Typhoon Haiyan (i.e., November 2013), we only found Landsat images suitable for this study. Hence, due to the limited spatial resolution of the data, detailed recovery analysis was not possible (e.g., extracting more land-use classes, such as building use and crop types). As also concluded by [PERSON] et al. (2014), land cover maps can provide the main information for decision-makers to have an overview of the damage and reconstruction/recovery processes, which are critical in the early stages of recovery. However, a comprehensive recovery assessment needs more details that can be obtained using land use maps extracted from very high-resolution images. Little work has been done to date in damage and recovery mapping of the selected area, and none of this previous research investigated the entire Leyte island. In addition, the available studies that focused on some selected small areas were based on very high-resolution images with different class definitions. Hence, the validation is only based on the accuracy assessment of the land cover classification results. However, since GEE also allows to upload user images for processing, it might be used to upload a very high-resolution image for validation purposes. Moreover, the change in types of crops and trees, or any detail about the recovered trees, can provide more information about the recovery process. For example, it is essential from the environmental and ecological points of view to understand whether the same trees that were denuded by the typhoon recovered, or whether the trees detected were newly planted after the disaster. More detailed information on the age of the trees can provide significant additional insights into the post-disaster recovery process. In conclusion, further studies are needed to investigate the potential of the more recently available higher resolution optical and SAR images (Sentinel 1 and 2 images) for most of the case studies (i.e., disasters) that occurred after those platforms became operational. In addition, SAR data have been shown to be a useful tool for extracting land cover and use classes such as distinguishing different crop and tree types (Stein  
However, it has been shown that this assumption is not correct for all cases, and further investigation is needed to clearly determine the relationship between the speed of the post-disaster recovery and resilience [6]. Although the Random Forest-based image classification produced a high accuracy rate, using more advanced methods (i.e., deep learning-based approaches) within the Google Earth API could help to improve the accuracy and would allow executing more advanced image classification and object detection tasks. Supplementary materials can be accessed at: [[http://www.mdpi.com/2076-3417/10/13/4574/s1](http://www.mdpi.com/2076-3417/10/13/4574/s1)]([http://www.mdpi.com/2076-3417/10/13/4574/s1](http://www.mdpi.com/2076-3417/10/13/4574/s1)). [PERSON] wrote the manuscript, designed and conducted the experiments. [PERSON] contributed to the design and developing the methodology, and conducting the experiments.