June 2026: Delivered API usability improvements and launched a new JavaScript client for the Azure Planetary Computer Pro, enhancing developer experience and enabling broader access to geospatial data via STAC API. No explicit major bug fixes recorded; focus was on documentation, naming consistency, and feature completeness. This work improves onboarding, discoverability, and end-user value across REST specs and SDKs.

May 2026 focused on delivering business-critical enhancements across Azure REST specs and the Python SDK, plus a GA release for the Azure Planetary Computer SDK. Key work targeted image processing capabilities, API surface simplification, and SDK usability improvements, with strong emphasis on tests, docs, and CI reliability to accelerate customer adoption and reduce maintenance burden. Highlights include a major capacity expansion for image outputs, API simplification by removing legacy models, client SDK usability refinements, and a GA-ready release aligned with the latest API specs.

April 2026 monthly summary for mikeharder/azure-rest-api-specs focused on delivering a GA API surface for Microsoft Planetary Computer Pro (Data-Plane) and tightening quality across the REST spec. Key outcomes include the stable API version 2026-04-15 with new endpoints for collections and items, deprecation of old preview routes, and alignment with stable codegen/validation pipelines. Improvements to example coverage, validation, and CI stability were implemented to reduce customer migration risk and support reliable data access.

February 2026 highlights for the Azure/azure-rest-api-specs repo: Implemented Flexible Bounding Box Representation for Mosaic and TilerInfoGeoJsonFeature by changing the boundingBox type from float32 to float32[] in the relevant schemas. This update enables multi-dimensional, more flexible bounding definitions and aligns the geospatial surface with evolving client needs. The change was implemented as commit 3de705b1f6ad03adaae250fda48fae13de1f5b6a (#40644), with collaboration from Karthick. This work improves data modeling robustness, reduces client-side parsing issues, and supports future geospatial feature enhancements.