# Post-Disaster Recovery Monitoring with Google Earth Engine
###### Abstract  
Post-disaster recovery is a complex process in terms of measuring its progress after a disaster and understanding its components and influencing factors. During this process, disaster planners and governments need reliable information to make decisions towards building the affected region back to normal (pre-disaster), or even improved, conditions. Hence, it is essential to use methods to understand the dynamics/variables of the post-disaster recovery process, and rapid and cost-effective data and tools to monitor the process. Google Earth Engine (GEE) provides free access to vast amounts of remote sensing (RS) data and a powerful computing environment in a cloud platform, making it an attractive tool to analyze earth surface data. In this study we assessed the suitability of GEE to analyze and track recovery. To do so, we employed GEE to assess the recovery process over a three-year period after Typhoon Haiyan, which struck Leyte island, in the Philippines, in 2013. We developed an approach to (i) generate cloud and shadow-free image composites from Landsat 7 and 8 satellite imagery and produce land cover classification data using the Random Forest method, and (ii) generate damage and recovery maps based on post-classification change analysis. The method produced land cover maps with accuracies >88%. We used the model to produce damage and three time-step recovery maps for 62 municipalities on Leyte island. The results showed that most of the municipalities had recovered after three years in terms of returning to the pre-disaster situation based on the selected land cover change analysis. However, more analysis (e.g., functional assessment) based on detailed data (e.g., land use maps) is needed to evaluate the more complex and subtle socio-economic aspects of the recovery. The study showed that GEE has good potential for monitoring the recovery process for extensive regions. However, the most important limitation is the lack of very-high-resolution RS data that are critical to assess the process in detail, in particular in complex urban environments. disaster; damage; recovery; monitoring; assessment; remote sensing; satellite imagery; Landsat; Google Earth Engine; Typhoon Haiyan; cloud computing +
Footnote †: journal: Applied  
cycle. Moreover, it has been considered as a proxy to evaluate resilience [4; 5], with resilient communities recovering faster after a disaster. However, a recent study argued that this assumption may not be true in all cases, and there is a need for more studies to explore the relationship [6]. Accordingly, providing information about the status of the damage and the reconstruction and recovery process after a disaster is vital to support decision-makers and planners, but also to support post-event risk updating efforts. Remote sensing (RS) data and techniques have been extensively used for different aspects of disaster risk management (DRM) [5], from quantification of social [7] and physical [8] vulnerabilities, to rapid damage assessments [9] using satellite [10], airborne [11], and Unmanned Aerial Vehicle (UAV) images [12], and numerous data analysis methods have been developed. Conversely, only recently have a small number of studies focused on the recovery phase [4; 13]. Recently, RS data also started to be used for disaster recovery monitoring; however, most of these studies used RS for physical recovery assessment [5]. For instance, [PERSON] et al. [14] developed an indicator-based methodology to monitor and evaluate post-disaster recovery based on high-resolution RS imagery, particularly IKONOS and QuickBird satellite images, in addition to field surveys and internet-based statistic data sets. They implemented image processing techniques for change detection (i.e., land cover changes) and building-based recovery/reconstruction analysis. They evaluated the potential of RS data for recovery assessment. In a different study, [PERSON] et al. [2] used the repeat photography method to evaluate post-Katrina recovery in Mississippi.