Change Detection from Remote Sensing to Guide OpenStreetMap Labeling

Author:

Albrecht Conrad M.,Zhang Rui,Cui Xiaodong,Freitag Marcus,Hamann Hendrik F.,Klein Levente J.ORCID,Finkler Ulrich,Marianno Fernando,Schmude Johannes,Bobroff Norman,Zhang Wei,Siebenschuh Carlo,Lu Siyuan

Abstract

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.

Publisher

MDPI AG

Subject

Earth and Planetary Sciences (miscellaneous),Computers in Earth Sciences,Geography, Planning and Development

Reference97 articles.

1. OpenStreetMaphttps://www.openstreetmap.org/

2. OpenStreetMap Editorhttps://www.openstreetmap.org/edit

3. Mask R-CNN

4. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

5. SegNethttps://mi.eng.cam.ac.uk/projects/segnet/

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