Mapflow.ai — new Roads model

What’s new?

Where on Earth users start their data processings using Mapflow Roads detection
  1. The new model can significantly better detect narrow unpaved roads, which are not well represented on many maps.
  2. It can handle various types of landscape patterns including rural areas, forests, northern regions with snow and swamps, agricultural fields, etc.
  3. In addition, the new model greater preserves the topological correctness of the road network by solving the occlusion problem causing by trees, overpasses and over obstacles in the imagery. Which means that the model’s intended to fill in the gaps in the output mask towards the production of the linked road graph — therefore less time for edits is required.

How it works?

  1. Most unpaved roads have very vague borders, which leads to fuzzy edges of a segmented mask.
  2. There are many occlusions, caused mainly by trees canopies that overlap the road surface in the satellite image, producing undesirable gaps in the output.
  1. Road surface extraction — it is a classical semantic segmentation problem and most of the other approaches include only this step.
  2. Road boundaries extraction is primarily aimed at refining the segmented surface.
  3. Road center line extraction makes it is easier for the model to understand which road parts should be connected or not.
Additional penalty for breaks in extracted center line helps model to better deal with the occluded road parts
Additional penalty incorrectly predicted boundaries helps to extract more narrow roads with more precise surface

More examples?

  1. Roads in forest
  2. Roads in fields
  3. Industrial zone
  4. Suburban zone

What’s next?

References

  • Mapflow.ai — The Geoalert platform for AI-mapping

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We apply Machine learning to automated analysis over Earth observation data

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We apply Machine learning to automated analysis over Earth observation data

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