train/hls_chips | Multi-Temporal Cloud Gap Imputation With HLS Imagery Across CONUS | Source Cooperative
Multi-Temporal Cloud Gap Imputation With HLS Imagery Across CONUS
This dataset contains temporal Harmonized Landsat-Sentinel imagery of diverse land covers across the Contiguous United States for the year 2022 along with binary cloud masks for the same area and year. This dataset's primary purpose is to train machine learning models for cloud gap imputation. The dataset contains 7,852 224x224x18 HLS scenes and 21,642 binary cloud masks of size 224x224.