A NEW APPROACH FOR MAPPING LAND USE / LAND COVER USING GOOGLE EARTH ENGINE: A COMPARISON OF COMPOSITION IMAGES

8 citations as recorded by crossref.

1. Influence of Image Compositing and Multisource Data Fusion on Multitemporal Land Cover Mapping of Two Philippine Watersheds N. Almarines et al. 10.3390/rs16122167
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3. Land-use/cover change and future prediction by integrating the ML techniques of random forest and CA-Markov chain model of the Ganges alluvial tract of Eastern India K. Roy et al. 10.1007/s10668-024-05545-x
4. Machine Learning Algorithms for Satellite Image Classification Using Google Earth Engine and Landsat Satellite Data: Morocco Case Study H. Ouchra et al. 10.1109/ACCESS.2023.3293828
5. Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools R. Ingle et al. 10.1038/s41598-023-38470-6
6. Provision of land use and forest density maps in semi-arid areas of Iran using Sentinel-2 satellite images and vegetation indices S. Eskandari & S. Bordbar 10.1016/j.asr.2024.10.060
7. Comparison of machine and deep learning algorithms using Google Earth Engine and Python for land classifications A. Nigar et al. 10.3389/fenvs.2024.1378443
8. Google Earth Engine-based mapping of land use and land cover for weather forecast models using Landsat 8 imagery M. Ganjirad & H. Bagheri 10.1016/j.ecoinf.2024.102498