From Historical OpenStreetMap data to customized training samples for geospatial machine learning

Room: Track 2
Academic Track

Sunday, 13:00 UTC
Duration: 20 minutes (plus Q&A)

session pad

full abstract - pdf

video on media.ccc.de
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  • Zhaoyan Wu (GIScience Research Group, Heidelberg University, Heidelberg, Germany and School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China)
  • Hao Li (GIScience Research Group, Heidelberg University, Heidelberg, Germany)
  • Alexander Zipf (GIScience Research Group, Heidelberg University, Heidelberg, Germany)

Recently, OpenStreetMap (OSM) shows great potentials in providing massive and freely accessible training samples to further empower geospatial machine learning activities. We developed a flexible framework to automatically generate customized training samples from historical OSM data, which in the meantime provide the OSM intrinsic quality measurements as an additional feature. Moreover, different satellite imagery APIs and machine learning tasks are supported within the framework.