Skip to content

Latest commit

 

History

History
63 lines (41 loc) · 3.08 KB

File metadata and controls

63 lines (41 loc) · 3.08 KB

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project mostly adheres to Semantic Versioning.

Changed

  • fail early if the user requests a bbox x resolution combination that would return a zero-dimension raster (#41)
  • reduced size of docker image by using python-slim, multi-stage build, and including only the required files

1.1.1 - 2025-10-08

Fixed

  • use all_touched pixels in the vector endpoint to ensure valid (i.e., non-NA) results even if polygons are smaller than the raster cell size (#39)

Changed

  • compute_vector endpoint now takes resolution as a body parameter instead of a query parameter, to match the design of the compute_raster endpoint (#40)
  • Python from version 3.11 to 3.13

1.1.0 - 2025-05-13

Changed

  • the default time range is now the last complete calendar year. This enables the utility to do more extensive caching as the results will be stable for 1y for the same region.
  • the resolution is now 90m x 90m by default but can be set by the client. This is done to reduce the processing unit consumption (see #35).
  • the output type for the naturalness and NDVI evalscripts is now 16 bit (which is then reverted in the client) to reduce processing unit consumption by reducing precision ( see #35).

Fixed

Added

  • the utility is now able to estimate the required PUs and retrieve the actually consumed PUs

1.0.0 - 2025-01-28

Added

  • first version of a Naturalness Utility: API functionality with client-input parameters to define AOI and time span for which an index is calculated (#4)
    • currently three indices (median NDVI, water, combination of both called naturalness) based on Sentinel-2 imagery are implemented
    • API endpoints return an index as a raster file and
    • its zonal statistics aggregate the given index for the geometries in the user-provided FeatureCollection using user-provided metrics