Early damage estimation in the immediate aftermath of a catastrophic earthquake is critical for effective disaster response and informed decision-making, especially in densely populated urban areas with high seismic activity, such as the Lima Metropolitan Area, Peru. This research focuses on designing and developing a web-based geographic information system for early earthquake damage estimation, featuring a four-stage workflow. Stage 0 generates pre-event damage estimations using earthquake scenarios, infrastructure datasets, and vulnerability models. Stage 1, initiated immediately after an earthquake, selects the closest pre-calculated scenario by comparing the peak ground acceleration values recorded by accelerometers with pre-estimated intensity measures. Stage 2 creates ShakeMaps by interpolating observed ground motion indices to produce the updated damage estimates based on real-time intensity distributions. A future Stage 3 will refine these estimates using post-event observed data. Data acquisition relies on open geospatial services and Peruvian governmental platforms, integrating datasets related to general buildings, essential buildings (schools and hospitals), transportation networks (bridges and roads), and lifeline systems (water supply and sewage). The system was implemented using open-source geospatial tools, providing a cost-effective, scalable solution for early-time disaster management in earthquake regions.

The availability of tsunami hazard and exposure information is crucial to support effective emergency response in coastal areas. This study presents an automated framework that integrates tsunami numerical simulation, geospatial exposure analysis, and WebGIS-based visualization to generate standardized hazard and exposure reports for decision support. Using a parallel implementation of the TUNAMI-N2 model, a 6-h tsunami simulation for the Peruvian coast can be completed in ~45 min, with hazard and exposure reports automatically published on a WebGIS platform within ~3 min. Application to the historical 1746 Lima tsunami demonstrates the system’s capability to quantify hazard and exposure for operational decision-making.

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.