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Clark Center for Geospatial Analytics

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Hamed Alemohammad

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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.
Published on 24 May 2024Public
hls
multitemporal
cloud
imputation
Multi-Temporal Crop Classification with HLS Imagery across CONUS
This dataset contains temporal Harmonized Landsat-Sentinel (HLS) imagery of diverse crop type and land cover classes across the Contiguous United States (CONUS) for the year 2022. The target labels are derived from USDA's Crop Data Layer (CDL). It's primary purpose is for training segmentation geospatial machine learning models. It includes 3,854 chips of 224 x 224 pixel area at 30m spatial resolution.
Published on 10 Aug 2023Public
crop type
land cover
segmentation
hls
agriculture