Open Issues Need Help
View All on GitHubAI Summary: Create a comprehensive test suite for the `sentle` Python package, validating its core functionalities such as data download, processing (cloud detection, snow masking, harmonization, merging, and temporal composites), and output to Zarr format. The tests should cover various parameter combinations and edge cases, ensuring robustness and reliability.
Sentinel-1 & Sentinel-2 data cubes at large scale (bigger-than-memory) on any machine with integrated cloud detection, snow masking, harmonization, merging, and temporal composites.
AI Summary: Refactor the `sentle.process` function to accept keyword argument dictionaries for Sentinel-1 and Sentinel-2 parameters separately. This improves code organization and maintainability by grouping related options and making all parameters optional. The refactoring should ensure that parameters within each dictionary are exclusively relevant to the respective satellite data.
Sentinel-1 & Sentinel-2 data cubes at large scale (bigger-than-memory) on any machine with integrated cloud detection, snow masking, harmonization, merging, and temporal composites.
AI Summary: The task is to modify the `sentle` Python package to make the reprojection type a configurable parameter. Currently, the package uses bilinear interpolation for reprojection; the update should allow users to select from all reprojection methods supported by the `rasterio` library, providing greater flexibility and control over the reprojection process.
Sentinel-1 & Sentinel-2 data cubes at large scale (bigger-than-memory) on any machine with integrated cloud detection, snow masking, harmonization, merging, and temporal composites.