Change Detection from Remote Sensing to Guide OpenStreetMap Labeling

• Published in: ISPRS international journal of geo-information Vol. 9; no. 7; p. 427
• Main Authors: Albrecht, Conrad M., Zhang, Rui, Cui, Xiaodong, Freitag, Marcus, Hamann, Hendrik F., Klein, Levente J., Finkler, Ulrich, Marianno, Fernando, Schmude, Johannes, Bobroff, Norman, Zhang, Wei, Siebenschuh, Carlo, Lu, Siyuan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2020
• Subjects: Artificial neural networks; big geospatial databases; Change detection; Computer vision; Deep learning; Detection; Digital mapping; geospatial change detection; Global positioning systems; GPS; image segmentation; Imagery; Infrastructure; Labeling; Neural networks; Open systems; OpenStreetMap data collection; Remote sensing; Roads & highways; Spatial data; Training
• ISSN: 2220-9964; 2220-9964
• DOI: 10.3390/ijgi9070427

The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.