Cross-analysis of Multisource Data for Geolocation of Non-georeferenced Urban Infrastructure Data

Geographic Information Systems (GIS) are reference tools for representing, storing, analyzing, and visualizing geolocated data, particularly those related to urban infrastructures such as water networks. In addition to GIS reference data, there exists a significant amount of complementary data, referred to here as external data, generally produced in specific contexts such as urban network maintenance. When properly exploited, these external data sources, which are rich in information, can enhance GIS and help address the issue of missing data. However, these external data are often not geolocated, which makes their integration into GIS particularly complex.

The main objective of this work is to propose artificial intelligence–based methodologies to geolocate non-georeferenced external data, particularly maps related to urban water networks, by leveraging multisource data cross-analysis. The proposed approach relies on the joint exploitation of geolocated GIS data and external data lacking geolocation. It consists in analyzing maps using object detection techniques to extract characteristic elements, such as buildings or specific structures, which are then matched with corresponding entities available in the relevant GIS. By exploring different geographic areas of the same spatial extent as the maps and assessing the degree of similarity between the extracted elements and those referenced in the GIS, the method enables the identification of the most plausible area of correspondence and, ultimately, the geolocation of the maps in question.

This work addresses several major challenges in the context of geolocating external data using GIS data. The first challenge concerns the identification and selection of relevant elements capable of effectively guiding the search within available GIS. The second challenge lies in accounting for the sometimes limited reliability of object detection systems during the matching process. The third challenge involves defining appropriate similarity measures and selecting sufficiently discriminative elements for the matching process. Finally, the fourth challenge is algorithmic in nature, given that a map generally represents only a limited portion of a GIS, which raises issues similar to those encountered in large-scale matching approaches.

Acknowledgments :
This work was supported by the CHIST-ERA project ATLAS "GeoAI-based augmentation of multi-source urban GIS" under grant numbers CHIST-ERA-23-MultiGIS-02 and ANR-24-CHR4-0005 (French National Research Agency).