Invasive species detection from high resolution aerial photographs

This project focuses on the automatic detection of invasive plant species along railway tracks. Initially, the project targets two highly invasive species: Asian Knotweed (Fallopia japonica) and Giant Hogweed (Heracleum mantegazzianum). ProRail, the Dutch railway infrastructure manager, collects yearly high-resolution helicopter imagery covering all railway tracks in the Netherlands. A deep learning-based classifier is applied to this imagery to detect the aforementioned invasive species. This work was presented as a poster at the 25th anniversary event of the Ambient Intelligence research group.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-08
DOI
https://doi.org/10.5281/zenodo.23190637
Primary Topic
Remote-Sensing Image Classification
Type
article
Field-Weighted Citation Impact
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article

Invasive species detection from high resolution aerial photographs

Bram Ton, Ronald Visser, Etto L. Salomons
Zenodo (CERN European Organization for Nuclear Research)
Remote-Sensing Image Classification
article

Invasive species detection from high resolution aerial photographs

Bram Ton, Ronald Visser, Etto L. Salomons
article en

Abstract

This project focuses on the automatic detection of invasive plant species along railway tracks. Initially, the project targets two highly invasive species: Asian Knotweed (Fallopia japonica) and Giant Hogweed (Heracleum mantegazzianum). ProRail, the Dutch railway infrastructure manager, collects yearly high-resolution helicopter imagery covering all railway tracks in the Netherlands. A deep learning-based classifier is applied to this imagery to detect the aforementioned invasive species. This work was presented as a poster at the 25th anniversary event of the Ambient Intelligence research group.

Zenodo (CERN European Organization for Nuclear Research)
Saxion (NL)
Openalex Percentile: Top 13%
Remote-Sensing Image Classification
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