# Vegetation Mapping by UAV Visible Imagery and Machine Learning

arXiv: https://arxiv.org/abs/2205.11061 · HTML: https://mlsanity.com/paper/2205.11061/

## 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.

## Builds on (most cited first, 2 of 2)

- [From parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations](https://mlsanity.com/paper/2105.09261/) (2021)
- [Texture image analysis and texture classification methods - A review](https://mlsanity.com/paper/1904.06554/) (2019)

## 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.