Next Article in Journal
Spatial Data Science
Next Article in Special Issue
Using OpenStreetMap Data and Machine Learning to Generate Socio-Economic Indicators
Previous Article in Journal
Geographical and Economic Factors Affecting the Spatial Distribution of Micro, Small, and Medium Enterprises: An Empirical Study of The Kujawsko-Pomorskie Region in Poland
Previous Article in Special Issue
Quality Verification of Volunteered Geographic Information Using OSM Notes Data in a Global Context
Article

Change Detection from Remote Sensing to Guide OpenStreetMap Labeling

IBM TJ Watson Research Center, 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(7), 427; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070427
Received: 15 April 2020 / Revised: 18 June 2020 / Accepted: 23 June 2020 / Published: 2 July 2020
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. View Full-Text
Keywords: OpenStreetMap data collection; remote sensing; geospatial change detection; image segmentation; artificial neural networks; big geospatial databases OpenStreetMap data collection; remote sensing; geospatial change detection; image segmentation; artificial neural networks; big geospatial databases
Show Figures

Figure 1

MDPI and ACS Style

Albrecht, C.M.; Zhang, R.; Cui, X.; Freitag, M.; Hamann, H.F.; Klein, L.J.; Finkler, U.; Marianno, F.; Schmude, J.; Bobroff, N.; Zhang, W.; Siebenschuh, C.; Lu, S. Change Detection from Remote Sensing to Guide OpenStreetMap Labeling. ISPRS Int. J. Geo-Inf. 2020, 9, 427. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070427

AMA Style

Albrecht CM, Zhang R, Cui X, Freitag M, Hamann HF, Klein LJ, Finkler U, Marianno F, Schmude J, Bobroff N, Zhang W, Siebenschuh C, Lu S. Change Detection from Remote Sensing to Guide OpenStreetMap Labeling. ISPRS International Journal of Geo-Information. 2020; 9(7):427. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070427

Chicago/Turabian Style

Albrecht, Conrad M., Rui Zhang, Xiaodong Cui, Marcus Freitag, Hendrik F. Hamann, Levente J. Klein, Ulrich Finkler, Fernando Marianno, Johannes Schmude, Norman Bobroff, Wei Zhang, Carlo Siebenschuh, and Siyuan Lu. 2020. "Change Detection from Remote Sensing to Guide OpenStreetMap Labeling" ISPRS International Journal of Geo-Information 9, no. 7: 427. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9070427

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop