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.

Data and Resources

Additional Info

Field Value
ID
GCS Poster
Filebin Poster
Version
Is live dataset
License https://creativecommons.org/licenses/by-sa/4.0/
Structured data license
Date created
Date published May 24, 2024
Date modified August 21, 2025
Languages
Same as
  1. https://source.coop//clarkcga/hls-multi-temporal-cloud-gap-imputation
Cite as
Creators
Creator 1
Type
Organization
ID
clarkcga
Identifier
Name
Clark Center for Geospatial Analytics
Email
URL
https://source.coop/clarkcga
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