# Geospatial

Geospatial workflows are needed when visual data must stay connected to real-world coordinates. Typical tasks include mapping infrastructure, monitoring land and vegetation, extracting road and building features, and preparing training data for location-aware AI systems.

We include this industry section in Use Cases to show how Supervisely solves practical geospatial problems end to end, not just isolated annotation tasks.

## When Geospatial Workflows Are Needed

Teams usually need geospatial workflows when they work with:

* Satellite or aerial imagery.
* Elevation layers such as DTM/DEM.
* Coordinate-aware vector data like OpenStreetMap features.
* Projects where annotations must be exported back to geodata formats.

## How Supervisely Helps

Supervisely provides a unified pipeline for geospatial computer vision:

* **Data acquisition** with ecosystem apps that can fetch satellite imagery, terrain data, and OSM features.
* **Multi-layer annotation** with interfaces such as Multiview and Overlay for side-by-side or blended inspection.
* **AI-assisted labeling** with Smart Tool and model-assisted workflows to speed up annotation.
* **Team collaboration and quality control** using labeling jobs, reviews, and performance tracking.
* **Export to geodata** with conversion back to OSM-compatible formats.

This approach allows teams to keep one consistent workflow from data collection to model-ready output.

## Start Here

* [Geospatial Data Annotation](/use-cases/geospatial/geospatial-data.md) - detailed step-by-step workflow in Supervisely.
* [Geospatial Images in Python SDK](https://developer.supervisely.com/getting-started/python-sdk-tutorials/images/geospatial-images) - corresponding Developer Portal tutorial for programmatic workflows.


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