February 2025 monthly summary for allenai/rslearn_projects: Focused on delivering robust vessel detection workflow improvements, expanding data sources, stabilizing job execution, and strengthening maintainability through documentation and tests. Key work spanned feature delivery, essential bug fixes, and foundational tooling updates that increase reliability and business value. The vessel detection pipeline now supports GeoJSON output, proper folder/directory creation, and rslearn-based path generation, with Sentinel-2 crop window handling split from the Landsat pipeline and dataset setup configured outside the Landsat workflow. Critical fixes include making Load Best Checkpoint validation fail when a checkpoint is missing, and deduplication/prepare-step fixes that improve reliability of alert extraction. New features include Hunter dataset integration and Beaker tooling, alongside ongoing work on forest loss WIP. Documentation and testing were expanded significantly, including viterbi smoothing docs and general docs, Landsat band notes, and broad component tests, driving maintainability, reproducibility, and faster iteration cycles.

January 2025 monthly summary focusing on business value and technical achievements across rslearn and rslearn_projects. Delivered end-to-end data ingestion, data source integrations, Azure-based experimentation, and governance improvements to accelerate deployment, improve reliability, and expand coverage for downstream analytics and modeling.

December 2024 monthly summary focusing on key accomplishments and business impact across rslearn and rslearn_projects. Deliveries span data quality, broadened data sources, scalable processing pipelines, automation improvements, and stronger testing/documentation. Key achievements (top 5-7): - Ingested Sentinel-1 and Sentinel-2 data sources via the Microsoft Planetary Computer STAC API, enabling unified data access and retrieval logic across rslearn. - CI workflow enhancement to authenticate with USGS Landsat token via environment variable, simplifying automation and reducing credential management risk. - Window class enhancements with is_layer_completed and mark_layer_completed APIs plus unit tests, enabling clearer layer-tracking and reliability. - Satlas data processing and publishing enhancements: new distributed worker pipeline integration, refactored Satlas prediction to monthly Sentinel-2 data, improved post-processing (non-maximum suppression, Viterbi smoothing), and enhanced data merging, vector tile generation, storage/publishing workflows, plus point label smoothing. - Beaker Pub/Sub worker pipeline improvements: Pub/Sub-based job management, dataset-specific configuration for marine infra and wind turbines, threading improvements for asynchronous processing, and support for shared memory in training jobs; dependencies updated. - Major bug fixes: data validation and integrity improvements (band name validation, missing Sentinel-2 XML handling, geometry validity), geometry splitting bug fix, and autoresume reliability fix when wandb config already exists. - Documentation, README updates, and testing improvements across projects to improve onboarding, usage clarity, and test coverage.

November 2024 proved a strong month for rslearn, delivering core reliability and performance improvements across rslearn_projects and rslearn, with tangible business value from faster pipelines, more robust parallel execution, and richer experiment visibility.

October 2024 focused on stabilizing build and CI pipelines, expanding cross-model evaluation capabilities, and improving data interoperability. Key improvements across two repositories delivered business value through reproducible builds, reliable experiments, and enhanced data handling, enabling faster iteration and more trustworthy results.