TOWARDS FINE-GRAINED ROAD MAPS EXTRACTION USING SENTINEL-2 IMAGERY

7 citations as recorded by crossref.

1. A deep learning approach to satellite image time series coregistration through alignment of road networks A. Pérez et al. https://doi.org/10.1007/s00521-023-09242-0
2. AI powered road network prediction with fused low-resolution satellite imagery and GPS trajectory N. Gengec et al. https://doi.org/10.1007/s12145-023-01201-6
3. National-Scale Detection of New Forest Roads in Sentinel-2 Time Series Ø. Trier & A. Salberg https://doi.org/10.3390/rs16213972
4. Super-Resolution Rural Road Extraction from Sentinel-2 Imagery Using a Spatial Relationship-Informed Network Y. Jia et al. https://doi.org/10.3390/rs15174193
5. A deep learning classification approach using high spatial satellite images for detection of built-up areas in rural zones: Case study of Souss-Massa region - Morocco M. Wahbi et al. https://doi.org/10.1016/j.rsase.2022.100898
6. Use of the SNOWED Dataset for Sentinel-2 Remote Sensing of Water Bodies: The Case of the Po River M. Scarpetta et al. https://doi.org/10.3390/s24175827
7. Multi-Class Strategies for Joint Building Footprint and Road Detection in Remote Sensing C. Ayala et al. https://doi.org/10.3390/app11188340