Multi-sensor data fusion for the study of historical heritage buildings: voxelization and deep learning
DOI:
https://doi.org/10.20868/ade.2024.5461Keywords:
fusion, multisensor, voxel, deep learning, pathologyAbstract
Pathology analysis in buildings, especially heritage structures, has advanced through geomatic sensors. The combination of active and passive sensors, such as laser scanners and cameras with various spectral sensitivities, produces detailed 3D models, spectral point clouds, and visual representations highlighting affected areas. This data fusion enables early detection and monitoring of cracks, deformations, and corrosion.
Despite these advances, challenges remain with calibration and accurate data integration, requiring expertise in remote sensing and structural analysis. This study used sensors like cameras, drones, thermal infrared cameras, and laser scanners on a historic building, generating point clouds that were merged into voxelized (3D unit) structures.
These voxelized structures allow deep learning algorithms, such as self-organizing maps, to identify pathologies and support intervention decisions. Results confirm that building issues were accurately isolated in the map, demonstrating the effectiveness of this methodology for studying pathologies and other phenomena in buildings.
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