# Post-Disaster Recovery Monitoring with Google Earth Engine
###### Abstract
They took photographs every six months over a three-year period. Then, by assigning scores to each scene in terms of change and recovery, they generated a map for recovery assessment for the entire region. Night-time light satellite images have been also used for damage and recovery analysis, and it has been demonstrated that there is a close relationship between light intensity and economic activity [15]. However, the night-time light satellite images are only useful for urban area recovery assessments. [PERSON] et al. [16] developed a model to assess the damage and early recovery using RS data and ground survey tools after the 2008 Wenchuan earthquake in China. The recovery step of their study included buildings, accessibility, power, and water livelihoods assessments. In their study, only the Normalized Differential Vegetation Index (NDVI) computation was implemented automatically, and other information, such as building change detection and accessibility assessments, were generated manually. [PERSON] and [PERSON] [17] studied urban recovery using geospatial data for the firework disaster in Enschede, the Netherlands, 2000. They mainly employed very high-resolution aerial images to extract information about the building morphology, such as building density, shape, and size, and concentration of road networks as proxies. In addition they proposed a proxy to measure the quality of housing based on the energy loss indicator of the buildings. [PERSON] et al. [18] used ground survey information in combination with RS, employing visual interpretation and binary classification methods to monitor post-disaster urban recovery. [PERSON] et al. [19] integrated RS (high-resolution satellite/aerial imagery) and ground observations to assess the recovery process in the Italian city of L'Aquila after the 2009 earthquake. They showed that using RS data for recovery monitoring reduces the required ground fieldwork. Recent studies have focused on the extraction of proxies for post-disaster recovery assessment by land cover and land use classification and change detection of very high-resolution images using advanced machine learning methods [20]. [PERSON] et al. [4] implemented a Support Vector Machine (SVM)-based method to produce land cover and land use classification maps for before, event/just after, and years after the disaster. They assessed the recovery processes by monitoring the changes in land cover and use maps. They also introduced the concept of positive and negative recoveries based on the observed change patterns. Furthermore, [PERSON] et al. [6] developed a conceptual framework for RS-based recovery monitoring and then integrated it with statistical economic analysis to assess the resilience-centered development interventions. They hypothesized that the speed of recovery is a proxy for resilience assessment; however, for some areas their results showed a negative relationship between image-derived speed of the recovery and resilience calculated based on household surveys. They employed the Extreme Gradient Boosting (XGBoost) method [21; 22] to classify the very high-resolution images to extract RS-based proxies. They concluded that although RS can provide useful information in addition to socio-economic survey data, the high cost of very high-resolution images and the need for high computational processing power are the limitations of utilizing RS data. Moreover, deep learning, in particular convolutional neural networks (CNN), has become the state-of-the-art method in computer vision and RS data analysis, and has also been used for post-disaster damage and recovery assessments [9; 23; 24]. For instance, a new image-patch generation approach was developed to train a CNN-based network simultaneously with multi-temporal satellite images and assess recovery [25]. In addition, free OpenStreetMap building footprints were employed to automatically generate training data from very high-resolution satellite images for a pixel-level deep learning method for post-disaster building database updating [26].