Conference Agenda

Smartphone photogrammetry offers a low-cost and accessible solution for monitoring surface deformation in urban environments. Subsidence, often caused by excessive groundwater extraction, leads to structural damage, ground fracturing, and long-term risks for cities like Mexico City. In the city center, average subsidence rates range from 9 to 10 cm per year, significantly affecting infrastructure and heritage sites. One of the most affected monuments is the Old Basilica of Guadalupe, a major historical and religious landmark built in 1709 and located in northern Mexico City. The basilica is part of the larger shrine complex dedicated to Our Lady of Guadalupe, one of the most visited Catholic pilgrimage sites in the world. It has suffered ground settlement since the early 20th century, with structural damage exacerbated by repeated seismic events, especially after the 2017 earthquake. This study evaluates the effectiveness of close-range digital photogrammetry using smartphone imagery to detect fracture evolution in the building. Two sets of images were acquired with a mid-range smartphone in April 2022 and March 2023, before and after the September 2022 earthquakes. The images were processed using Structure from Motion (SfM) techniques to generate 3D models for visual comparison and analysis of deformation patterns. Results show that, despite limitations in image resolution and lighting conditions, smartphone photogrammetry can effectively capture fracture propagation and tilt in architectural features. This method provides a practical and non-invasive tool for structural monitoring in sensitive or restricted-access sites. Its successful application in a major heritage building demonstrates the potential of smartphone-based photogrammetry to support routine damage assessments and long-term preservation strategies in subsidence-prone urban areas, particularly where conventional equipment is not feasible.

In this study, bathymetric models were developed in coastal waters of the Mexican Caribbean, using an empirical algorithm and satellite images from Landsat 9, Sentinel-2 and SuperDove CubeSats. The model used is based on the relationship between the blue and green spectral bands and was adjusted with depth data taken in situ in the coastal area of Mahahual, Quintana Roo. The study showed that the model enables the estimation of depth relatively accurately, up to 20 meters. Landsat 9 presented the best performance, with an RMSE error of 2.0 meters, followed by SuperDove (2.5 m) and Sentinel-2 (3.0 m). The most significant errors occurred at shallow depths of less than 5 m or greater than 20 m. Preprocessing of the images, particularly sunlight correction and spatial filtering, was crucial to improving the results. Remote sensing offers a very economical alternative for mapping bathymetry in shallow, low-turbid coastal areas.