Production-grade guides for building geospatial data annotation pipelines that feed AI/ML training.

Visit the site → www.geospatialannotation.com

Geospatial machine learning lives or dies on the quality of its labels. Satellite and drone imagery arrives with heterogeneous projections, multi-spectral bands, and gigabyte-scale extents that generic computer-vision tooling was never designed for. This site is a deep, practitioner-focused reference for the engineers, spatial data scientists, and GIS annotation teams who turn that raw imagery into clean, versioned, model-ready training data — with runnable Python, real coordinate-system handling, and the validation gates that keep a bad batch of labels from ever reaching a training run.

Every guide is written to be used, not skimmed: concept grounding first, then production-ready code with pinned dependency versions, then the spatial-specific failure modes that only show up at scale.

The material is organized into four connected areas, each with in-depth guides and focused how-tos:

• Geospatial Annotation Fundamentals & Architecture — coordinate reference systems, ROI label taxonomies, confidence scoring, vector vs. raster workflows, and the spatial data formats (Cloud-Optimized GeoTIFF, GeoParquet, STAC) that hold a pipeline together.
• Labeling Workflows & Toolchain Integration — Label Studio, CVAT, and QGIS integration, model-assisted pre-labeling with foundation models, human review cycles, export-format validation for COCO/YOLO/GeoJSON, and CI/CD gates for annotation datasets.
• Dataset Versioning & Spatial Data Sync — DVC for large imagery, metadata preservation, SHA-based change tracking, cross-remote sync, and rollback strategies for corrupted spatial datasets.
• Active Learning & Model Feedback Loops — uncertainty sampling on spatial tiles, automated retraining triggers, and distribution-drift detection that focuses annotation effort where the model is weakest.

• Runnable, version-pinned code. Python 3.10+ with explicit type hints, using geopandas, pyproj, rasterio, and shapely the way production pipelines actually use them.
• Coordinate systems taken seriously. Reprojection, datum-transformation grids, roundtrip testing, and the projection mistakes that silently corrupt IoU scores and training labels.
• Validation you can wire into CI. Schema enforcement, geometry checks, and class-balance monitors designed to run as GitHub Actions or DVC pipeline stages.
• Original, hand-authored diagrams on every guide, built to stay legible in light and dark themes.

A fast, dependency-light static site with no client-side framework:

• Eleventy static site generator, Nunjucks + Markdown.
• Hand-authored inline SVG diagrams; Prism for code highlighting.
• Structured data (Article, BreadcrumbList, HowTo, FAQPage) on every page.
• Deployed on Cloudflare Pages.

npm install
npm run serve   # local dev server at http://localhost:8080
npm run build   # production build into _site/

Commits to this repository are made under a single maintainer identity, and commit messages carry no co-author trailers. Please keep that convention if you open a pull request.

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