COMPARISON OF OBJECT BASED MACHINE LEARNING CLASSIFICATIONS OF PLANETSCOPE AND WORLDVIEW-3 SATELLITE IMAGES FOR LAND USE / COVER

7 citations as recorded by crossref.

1. The Feasibility of Monitoring Great Plains Playa Inundation with the Sentinel 2A/B Satellites for Ecological and Hydrological Applications H. Tripp et al. https://doi.org/10.3390/w14152314
2. Using Landsat-5 for Accurate Historical LULC Classification: A Comparison of Machine Learning Models D. Krivoguz et al. https://doi.org/10.3390/data8090138
3. Comparing WorldView-2 and PlanetScope Imagery to Mapping Housing Types Using GEOBIA A. Hafiudzan et al. https://doi.org/10.1088/1755-1315/1264/1/012007
4. Planetscope Nanosatellites Image Classification Using Machine Learning M. Anul Haq https://doi.org/10.32604/csse.2022.023221
5. Integrating UAV-Derived Information and WorldView-3 Imagery for Mapping Wetland Plants in the Old Woman Creek Estuary, USA M. Islam et al. https://doi.org/10.3390/rs15041090
6. Spatio‐temporal analysis of land use/land cover change and urban growth dynamics of Silchar City, India using very high‐resolution satellite data and the Shannon entropy model D. Mohabey et al. https://doi.org/10.1002/ldr.4608
7. Application of nanosatellites PlanetScope data to monitor crop growth S. Kokhan et al. https://doi.org/10.1051/e3sconf/202017102014