A METHODOLOGY FOR ASSESSING OPENSTREETMAP DEGREE OF COVERAGE FOR PURPOSES OF LAND COVER MAPPING

6 citations as recorded by crossref.

1. Weighted split-flow network auxiliary with hierarchical multitasking for urban land use classification of high-resolution remote sensing images G. He et al. https://doi.org/10.1080/01431161.2022.2143734
2. Insect diversity is a good indicator of biodiversity status in Africa T. Landmann et al. https://doi.org/10.1038/s43247-023-00896-1
3. Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence K. Bhuyan et al. https://doi.org/10.1007/s11069-022-05612-4
4. Mapping land use impact of photovoltaic farms via crowdsourcing in the Province of Lecce (Southeastern Italy) G. Mauro & V. Lughi https://doi.org/10.1016/j.solener.2017.06.046
5. Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data S. Borkowska et al. https://doi.org/10.3390/app13137525
6. Analysing the Potential of OpenStreetMap Data to Improve the Accuracy of SRTM 30 DEM on Derived Basin Delineation, Slope, and Drainage Networks E. Monteiro et al. https://doi.org/10.3390/hydrology5030034