Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery

Abstract

Rapid urbanization and increasing population in cities with a large portion of them settled in deprived neighborhoods, mostly defined as slum areas, have escalated inequality and vulnerability to natural disasters. As a result, monitoring such areas is essential to provide information and support decision-makers and urban planners, especially in case of disaster recovery. Here, we developed an approach to monitor the urban deprived areas over a four-year period after super Typhoon Haiyan, which struck Tacloban city, in the Philippines, in 2013, using high-resolution satellite images and machine learning methods. A Support Vector Machine classification method supported by a local binary patterns feature extraction model was initially performed to detect slum areas in the pre-disaster, just after/event, and post-disaster images. Afterward, a dense conditional random fields model was employed to produce the final slum areas maps. The developed method detected slum areas with accuracies over 83%. We produced the damage and recovery maps based on change analysis over the detected slum areas. The results revealed that most of the slum areas were reconstructed 4 years after Typhoon Haiyan, and thus, the city returned to the pre-existing vulnerability level.

Remote sensing as a tool for natural disaster risk analysis: a bibliometric review

“…Aunado a ello, Zheng et al (39) sostiene que la teledetección y las imágenes satelitales pueden minimizar el tiempo y los costos relacionados con las estrategias de mitigación y reducción del riesgo, en particular ante desastres naturales como huracanes, inundaciones, sequías y terremotos. Por su parte, Ghaffarian et al (40) reconocen que estos datos pueden utilizarse para determinar la mejor forma de actuar y clasificar las zonas que necesitan ayuda e identificar las infraestructuras críticas que requerirán mantenimiento, reparación o sustitución. En general, las imágenes por satélite son indispensables, cuando se trata de prepararse, planificar y responder a los desastres naturales.…”

Using Local Knowledge and Remote Sensing in the Identification of Informal Settlements in Riyadh City, Saudi Arabia

“…Chang et al [25] have shown that using OBIA techniques can substantially overcome issues associated with the per-pixel method, and these are capable of more precisely defining a spatially complex urban area by successfully distinguishing between settlement types [20,24,26]. Several researchers, including Ghaffarian and Emtehani [27], Kohli et al [28] and Jovanović et al [29], have studied the use and effectiveness of OBIA. They concluded that OBIA produces highly accurate results.…”

Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring

“…We use a real-world application to demonstrate the usefulness of slum mapping models with uncertainty quantification. We use the models to track changes in the areas of slums, a task that has significant applications to humanitarian aid [30]. We used satellite images from different years and used our models to predict where the slums are situated within the images.…”

“…Using remote sensing imagery has several advantages over conventional survey-based methods like taking a census, including but not limited to cost-efficiency, global coverage, and higher resolution in both space and time [20,27,29]. Furthermore, these advantages are augmented by the advancement in computer vision, including machine learning [30] and deep learning [14,28,31,32]. There are different levels of accessibility of satellite images, with some images being free to use and publicly accessible, and these are the most desirable to use in the often less well-funded settings that unfortunately are more common within LMICs.…”