ANNUAL PAST-PRESENT LAND COVER CLASSIFICATION FROM LANDSAT USING DEEP LEARNING FOR URBAN AGGLOMERATIONS

抄録

 Historical land cover data is crucial for understanding urbanization dynamics, climate modeling, and monitoring water resources. Following recent advancements in deep learning for processing Landsat archive data, prior studies have released high-resolution historical land cover maps on a global scale. However, these works often present prediction results limited to specific periods of coverage, which hinders their utility in conducting time series analysis across different urban agglomerations. To address this issue, we propose deep-learning models for land cover classification from Landsat images at a 30-meter spatial resolution. Our models are specifically designed for urban areas and are trained to be compatible with the sensors used in the Landsat series from 1972 to the present. Experimental results demonstrate that our models are highly effective in predicting land cover maps in new cities, particularly in built-up land and water regions. Our research provides pretrained models for land cover classification, facilitating future studies in related fields.