Land cover classification: Datasets & Models
Automatically classify land cover types from aerial and satellite imagery, assigning each image or tile to categories such as forest, water, urban, or agriculture. Built for geospatial teams that need consistent labeling across large areas.
Land cover classification
This workflow performs land cover detection by running image classification on aerial, drone, and satellite tiles, assigning each input a single dominant land cover label. It detects categories such as forest, cropland, built-up area, water, barren land, and grassland, taking individual images or gridded map tiles as input.
Try it out
Powered by general detection model
Run on custom image
Drop an image here or click to upload
Why automate land cover classification
Manually interpreting land cover across thousands of image tiles is slow, subjective, and difficult to keep consistent between analysts. For drone and geospatial teams monitoring deforestation, urban expansion, crop coverage, or watershed change, automating this classification turns weeks of photo interpretation into a batch inference job. A trained ViT model applies the same criteria to every tile, so results are reproducible and easy to audit over time. That consistency matters when outputs feed into environmental reporting, zoning decisions, or change-detection pipelines.
Key benefits
Automated land cover classification scales from a single survey flight to nationwide satellite mosaics without adding analyst headcount, and it plugs into existing GIS and imagery pipelines through standard tiling and inference APIs. Because the model runs on the same aerial and satellite feeds you already collect, you can reclassify historical archives and new captures with identical logic. The result is faster turnaround, consistent labels, and a foundation for downstream change detection and monitoring.
How to use it
- Upload an image: Drop any photo into the widget above to try land cover classification for free, right on this page.
- Preview instant results: The model detects forest, water body, cropland, urban building, barren land within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train Classification (ViT) on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for land cover classification
When you're ready to build a production model for land cover classification, we recommend training Classification (ViT). Unlike the ready-to-use model in the demo above, you fine-tune it on your own labeled images for higher accuracy, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Land Cover Classification project to Roboflow Agent.