Lima, Peru's capital, has about 9.6 million inhabitants and keeps attracting more residents searching for a better life. Many citizens, without access to housing subsidies, live in informal housing and shack settlements. A typical social phenomenon in Lima is the sudden illegal occupation of areas for urban settlements. When such areas are unsafe against natural hazards, it is important to relocate such a population to avoid significant future losses. In this communication, we present an application of Sentinel-1 synthetic aperture radar (SAR) images to map the extension of a recent occupation of an area with unfavorable soil conditions against earthquakes.

Traditional tsunami vulnerability assessments often rely on empirical models and field surveys, which can be time-consuming and have limited accuracy. In this study, we propose a novel approach that integrates high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry with numerical simulation to improve vulnerability assessment efficacy in Ancon Bay, Lima, Peru, by using the Papathoma Tsunami Vulnerability Assessment (PTVA-4) model. For this purpose, a detailed 3D representation of the study area was generated using UAV-based oblique photogrammetry, enabling the extraction of building attributes. Additionally, a high-resolution numerical tsunami simulation was conducted using the TUNAMI-N2 model for a potential worst-case scenario that may affect the Central Peru subduction zone, incorporating topographic and land-use data obtained with UAV-based nadir photogrammetry. The results indicate that the northern region of Ancon Bay exhibits higher relative vulnerability levels due to greater inundation depths and more tsunami-prone building attributes. UAV-based assessments provide a rapid and detailed method for evaluating building vulnerability. These findings indicate that the proposed methodology is a valuable tool for supporting coastal risk planning and disaster preparedness in tsunami-prone areas.

The impact of natural hazards can be effectively mitigated through preliminary assessments that combine geospatial and modeling approaches. Among these, tsunami exposure mapping plays a crucial role in coastal risk evaluation. This study develops a methodological framework for tsunami exposure assessment by integrating numerical simulation and geospatial analysis. Two tsunamigenic seismic scenarios were considered: (i) a historically documented event and (ii) a plausible large-magnitude earthquake off the Pacific coast of Lima, Peru. Tsunami propagation and inundation were simulated using the TUNAMI-N2 model to generate detailed inundation maps that delineate the spatial extent and inundation depth. The temporal evolution of tsunami impact distribution was analyzed using arrival-time maps and synthetic tide gauges, strategically located along the coastline to record wave amplitude variations over time. The population exposed to tsunami hazard was quantified using national census demographic data, while critical buildings were identified from official geospatial databases. This integration provides refined insights into tsunami hazard dynamics and supports evidence-based coastal risk management and urban growth.

Peru faces a significant tsunami hazard due to its location along the Pacific Ring of Fire. Historical megathrust earthquakes and their resulting tsunamis have caused severe damage, highlighting the need for improved warning systems. This study investigates potential tsunami impacts along the central Peruvian coast—including the regions of Ica, Callao, Lima, and Ancash—using numerical simulations. To enable rapid and efficient simulations, we developed gWave-CPU, a parallelized version of the TUNAMI-N2 model created at Tohoku University. Using 90-m-resolution topographic and bathymetric data in combination with census data, we assessed population exposure under two seismic scenarios: a plausible event based on interseismic coupling and a historical scenario simulating the 1746 tsunami. Under the historical scenario, the exposed population was estimated at 320,128, with the highest concentrations in Callao and Lima. Numerical simulations of four hours of tsunami propagation and inundation were conducted using our parallelized implementation, reducing computation time to 68 minutes—a 26.3-fold speedup compared to the conventional model. The results demonstrate that tsunami inundation and population exposure in this region can be efficiently estimated using the proposed approach, providing a valuable contribution to tsunami hazard assessment, management, and emergency preparedness along Peru’s central coast.

Seismic risk assessment is essential for reducing earthquake-related damage in urban areas. Accurate recognition of building features is a key factor in evaluating seismic vulnerability, yet traditional manual inspection methods are inefficient and prone to error. This study proposes a deep learning-based framework using convolutional neural networks for automated instance segmentation of building features to support seismic risk estimation in Lima, Peru, a seismically active region with diverse architectural styles. Leveraging state-of-the-art models like Mask R-CNN, the framework identifies and segments structural components such as walls, windows, and columns from building imagery. By integrating geospatial data and remote sensing technologies, the proposed approach enhances seismic risk evaluations through automated, scalable, and precise feature recognition. Despite challenges such as occlusions, varying lighting conditions, and architectural diversity, the model aims to adapt through specialized training tailored to Lima’s urban landscape, contributing to more efficient disaster preparedness and response in earthquake-prone regions.

Estimating building heights is essential for urban planning, building resilience, and disaster risk assessment. This study introduces a method for deriving building heights using stereo pairs of high-resolution optical satellite images. A 3D point cloud was generated through stereo matching of two optical satellite images, and a high-resolution digital surface model was constructed to estimate building heights. In the absence of detailed building outlines, building height was calculated by subtracting the elevation outside the lot outline from that within the lot outline. Estimated building heights were then converted to floor numbers for evaluation. Validation involved comparing estimated floor numbers with manually counted floor numbers from field surveys and a 3D model generated from UAV imagery. Results indicated that 42% of buildings had accurately estimated floor numbers, and 82% were within a one-floor error margin. This approach offers a rapid, cost-effective solution for height estimation, especially in areas lacking detailed cadastral or LiDAR data.