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AI-assisted mapping of crop fields using free Sentinel imagery


  1. Hire a full team of professional cartographers for a season/project
  2. Hope that they will map everything fast enough
Fig. 1 — Example output of field’s boundary extraction (polygon feature) performed by our AI model on Sentinel 2 imagery

So what if we could combine the speed of AI and human accuracy?

  • approx. 4 500 polygons of crop fields;
  • each polygon has about 100 vertices;
  • the Sentinel-2 AI-mapping pipeline processes a single image of this area in ~ 30 min;
  • usually several images are required to cover all fields in AOI (to avoid clouds, shadows, etc…). We used 6 images in our example which makes 3 hours of processing for the whole region.
Example area in Belgorod region (Russia).
Testing area in Belgorod region (Russia)

Case 1: Count the number and the area size of all cultivated fields in the territory

Fig 2. Search results and preview of Sentinel-2 using Mapflow-QGIS plugin
Fig. 3 — Segmentation mask of crop fields, extracted by AI from Sentinel-2 image

Case 2: Inventorization of all fields in the territory

  1. not to miss objects, that were cultivated at different times (see Fig. 4(a, b))
  2. to ensure the full coverage of the territory (in case of clouds, haze, snow etc. , see Fig. 4(c))
Fig. 4— Field masks extracted by our AI models in different conditions
  • 6 images to process,
  • 1 cartographer*,
  • iterative AI-mapping guideline for the cartographer

A sneak peek to the iterative AI-mapping workflow 👇

AI-mapping for crop land maps tutorial
  1. Selection of the successful polygons produced by AI from multiple satellite images
  2. Minimizing the number of clicks for cartographer

In conclusion:

  • 🤞The new model of Sentinel-2 fields-mapping is successfully implemented into Mapflow and available for all users of the Mapflow-QGIS plugin. You can try it along with the high resolution model that we released before
  • We’ve released Mapflow-QGIS 1.6 🎉🎉🎉 — the main new feature is implementation of Catalog & Processing of Sentinel imagery
    (Sentinel search is powered by SkyWatch API 🙏— cool service and imagery provider, which we will tell much more about next time)
  • ❗️We designed the benchmark of an experimental agriculture territory showing that AI-mapping can speed up the whole process x80 times
  • ❗️❗️❗️We are looking for brave cartographers and developers who are ready to try iterative AI-mapping workflow — look at the guide and if you want to get more materials or suggest some edits, don’t hesitate to contact us
  • ✍️ 🤔 Don’t forget that apart from this service Mapflow provides Hi-res imagery model of fields mask extraction and roads mask extraction that can be useful for your overall agriculture mapping project — check related materials

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We apply Machine learning to automated analysis over Earth observation data