mlsanityData to 14 Oct 2026

Vegetation Mapping by UAV Visible Imagery and Machine Learning

arXiv 2205.11061 ↗First posted 23 May 2022cs.CV stat.MLcited by 0

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Abstract (arXiv)

An experimental field cropped with sugar-beet with a wide spreading of weeds has been used to test vegetation identification from drone visible imagery. Expert masked and hue-filtered pictures have been used to train several Machine Learning algorithms to develop a semi-automatic methodology for identification and mapping species at high resolution. Results show that 5m altitude allows for obtaining maps with an identification efficiency of more than 90%. Such a method can be easily integrated to present VRHA, as much as tools to obtain detailed maps of vegetation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 40 references that are in the map. First posted on arXiv 23 May 2022.

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2 of 40 references.

References · 2

  1. 2021From parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observationscited by 11
  2. 2019Texture image analysis and texture classification methods - A reviewcited by 10

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