Urban areas in Lima, Peru, grow fast and with almost no control. Furthermore, resources to monitor urban areas are limited in Peru. Thus, the evolution of exposure of urban areas to natural hazards is highly uncertain. In this paper, we report a practical use of deep learning-based land use and land cover mapping to quantify, with unprecedented accuracy, the increment in exposure against floods in the District of Ate, Lima, Peru. We use Google Satellite imagery recorded in 2002 and 2023, generate land use maps, and quantify the increment of urban areas in the floodplain of the Rimac River. Results show an increment of exposure to floods of about 10.3% after 21 years.

Remote sensing has been used extensively to identify the extent of floods. However, few studies have addressed the estimation of inundation depth, which would provide a deeper understanding of the affected areas. This paper reports a step-by-step application of a novel method to estimate inundation depths during a flood in Mabi town, Okayama Prefecture, Japan 2018. The method is based on the solution of a nonlinear programming problem, in which the flood extent, computed from SAR imagery, is represented as a sparse linear combination of water bodies calculated from a digital elevation model. The results show a good agreement with observations on the field survey and can be implemented in a fully automatic framework.

In this paper, we demonstrate the application of a new methodology for rapid identification of damaged areas to the case of the 2016 Kumamoto earthquake sequence. The method aims to integrate Synthetic Aperture Radar (SAR) imagery, fragility functions, and intense ground motion maps to calibrate a neural network changes detector in SAR images. All the necessary data was collected within a remarkably short period, maintaining relevance to real-world applications. Our findings indicate that minimal labeled data, such as from just six collapsed buildings, is sufficient for optimal neural network calibration. Upon validation with third-party data, this approach achieved an 84% accuracy rate, suggesting its potential as an effective tool during the crucial post-earthquake response phase, as it can provide decision-makers with crucial information to efficiently organize and allocate resources, such as sending aid, food, and rescue squads to the most severely affected areas.

We show the application of an automatic collection of training samples for the identification of flooded buildings. The method is based on a near real time estimation of the flooded area using in-place sensors and a numerical simulation. Then, microwave remote sensing images are used to improve the accuracy of the extent of the flooded area. The floods produced during the 2018 heavy rainfalls in the town of Mabi is reported as case study. The results are consistent with the flood map provided by a third party.

Previous applications of machine learning in remote sensing for the identification of damaged buildings in the aftermath of a large-scale disaster have been successful. However, standard methods do not consider the complexity and costs of compiling a training data set after a large-scale disaster. In this article, we study disaster events in which the intensity can be modeled via numerical simulation and/or instrumentation. For such cases, two fully automatic procedures for the detection of severely damaged buildings are introduced. The fundamental assumption is that samples that are located in areas with low disaster intensity mainly represent nondamaged buildings. Furthermore, areas with moderate to strong disaster intensities likely contain damaged and nondamaged buildings. Under this assumption, a procedure that is based on the automatic selection of training samples for learning and calibrating the standard support vector machine classifier is utilized. The second procedure is based on the use of two regularization parameters to define the support vectors. These frameworks avoid the collection of labeled building samples via field surveys and/or visual inspection of optical images, which requires a significant amount of time. The performance of the proposed method is evaluated via application to three real cases: the 2011 Tohoku-Oki earthquake–tsunami, the 2016 Kumamoto earthquake, and the 2018 Okayama floods. The resulted accuracy ranges between 0.85 and 0.89, and thus, it shows that the result can be used for the rapid allocation of affected buildings.