GEOMAPI: Processing close-range sensing data of construction scenes with semantic web technologies
Abstract
A joint API to standardize geomatic data storage and processing. Developed by the Geomatics research group at the KU Leuven
Workflow
Bibtex
@article{BASSIER2024105454,
title = {GEOMAPI: Processing close-range sensing data of construction scenes with semantic web technologies},
journal = {Automation in Construction},
volume = {164},
pages = {105454},
year = {2024},
issn = {0926-5805},
doi = {https://doi.org/10.1016/j.autcon.2024.105454},
url = {https://www.sciencedirect.com/science/article/pii/S0926580524001900},
author = {Maarten Bassier and Jelle Vermandere and Sam De Geyter and Heinder De Winter},
keywords = {Geomatics, Semantic Web Technologies, Construction, Close-range sensing, BIM, Point clouds, Photogrammetry},
abstract = {The AEC industry struggles with integrating close-range sensing data like photogrammetric and LiDAR outputs due to the vast volume of unstructured 2D and 3D data, hindering tasks like digital twinning and construction monitoring. Although semantic web technologies have effectively managed diverse resources in other domains, their potential in handling construction-related sensing data remains unrealized, further complicated by the absence of a comprehensive data management framework for construction projects. This paper bridges the gap between various close-range sensing resources and AEC industry tasks by integrating Linked Data RDF Graphs with advanced geomatics algorithms. Experimental results demonstrate that this close-range linked data methodology effectively combines sensory data processing with RDF Graphs, thereby enabling more efficient resolution of construction process queries. The GEOMAPI framework significantly improves access to close-range sensing data for construction stakeholders, facilitate complex geomatics computations, and stores multi-temporal analysis results, thereby advancing the capabilities in the AEC sector.}
}