4dcca7f7-b67f-4215-9a8b-4f2fd38cc548 https://ipt.gbif-test.org/resource?r=biodiversa_camalien_be Bram D'hondt Research Institute for Nature and Forest (INBO) bram.dhondt@inbo.be 0000-0002-1330-1457 Frédérique Steen Research Institute for Nature and Forest (INBO) frederique.steen@inbo.be 0000-0001-5165-8644 Axel Neukermans Research Institute for Nature and Forest (INBO) axel.neukermans@inbo.be 0000-0003-0272-9180 Tim Adriaens Research Institute for Nature and Forest (INBO) tim.adriaens@inbo.be 0000-0001-7268-4200 Lars Dalby Aarhus University lars@ecos.au.dk 0000-0002-7270-6999 Mark A.K. Gillespie Aarhus University markg@ecos.au.dk 0000-0001-9808-4836 Kavi Askholm Mellerup Aarhus University kavi@ecos.au.dk Toke T. Høye Aarhus University tth@ecos.au.dk 0000-0001-5387-3284 Pierre Bonnet CIRAD, AMAP pierre.bonnet@cirad.fr 0000-0002-2828-4389 Alexis Joly Inria alexis.joly@inria.fr 0000-0002-2161-9940 Sanne Govaert Research Institute for Nature and Forest (INBO) sanne.govaert@inbo.be 0000-0002-8939-1305 Bram D'hondt Research Institute for Nature and Forest (INBO) bram.dhondt@inbo.be 0000-0002-1330-1457 Lars Dalby Aarhus University lars@ecos.au.dk 0000-0002-7270-6999 Mark A.K. Gillespie Aarhus University markg@ecos.au.dk 0000-0001-9808-4836 Toke T. Høye Aarhus University tth@ecos.au.dk 0000-0001-5387-3284 Sanne Govaert Research Institute for Nature and Forest (INBO) sanne.govaert@inbo.be 0000-0002-8939-1305 2026-01-27 en IAS pilot - Observations of alien plant species using a vehicle-mounted camera system in Flanders (Belgium) is an occurrence dataset published by the Research Institute for Nature and Forest (INBO). It is part of the Biodiversa IAS pilot project (2023-2027), that implemented the monitoring of invasive alien plants using a vehicle-mounted camera system in various countries across Europe. Specifically, the CamAlien camera system (Dyrmann et al. 2024) was mounted on a car to capture images of the roadside vegetation. Data have been standardized to Darwin Core. Issues with the dataset can be reported at https://github.com/inbo/ias-pilot/issues.Images from the cameras were processed with image recognition algorithms provided through Pl@ntNet (Espitalier et al. 2025). For the current dataset, only pre-selected species of regional importance were retained, and occurrences (images) with insufficient confidence were filtered out.We have released this dataset to the public domain under a Creative Commons Zero waiver. We would appreciate it if you follow the INBO norms for data use (https://www.inbo.be/en/norms-data-use) when using the data. If you have any questions regarding this dataset, don't hesitate to contact us via the contact information provided in the metadata or via opendata@inbo.be.This dataset was published as open data for the IAS pilot project (funded by Biodiversa+) and the OneSTOP project (funded by Horizon Europe), with technical support provided by the Research Institute for Nature and Forest (INBO). Occurrence GBIF Dataset Type Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_type_2015-07-10.xml Observation GBIF Dataset Subtype Vocabulary: http://rs.gbif.org/vocabulary/gbif/dataset_subtype.xml CamAlien, invasive species, alien species, non-native species, IAS, plants n/a To the extent possible under law, the publisher has waived all rights to these data and has dedicated them to the Public Domain (CC0 1.0). Users may copy, modify, distribute and use the work, including for commercial purposes, without restriction. Creative Commons Zero v1.0 Universal https://spdx.org/licenses/CC0-1.0.html CC0-1.0 Flanders, Belgium 4.2162 4.9827 51.0716 50.7051 2024-07-18 2024-09-09 5 non-native species of vascular plants species Ailanthus altissima species Impatiens glandulifera genus Reynoutria sp. species Robinia pseudoacacia species Rosa rugosa asNeeded Bram D'hondt Research Institute for Nature and Forest (INBO) bram.dhondt@inbo.be 0000-0002-1330-1457 Partners from the project consortium defined the set of target species (invasive alien plant species of interest), and agreed on a generic sampling plan for the coming field season. The CamAlien camera system was mounted on a vehicle. The vehicle was driven along the road infrastructure, with the CamAlien system running. The resulting images were shared with the lead partner (Aarhus University), for storage and first-level edits. The images were transferred to Pl@ntNet, which returned classifications (identifications) of the target species in each of the images. The returned data were processed, by filtering on the confidence level threshold, or any other step deemed necessary to increase reliability. The study aimed to detect a set of predetermined invasive plant species in roadside verges on a regional scale during the field season of several consecutive years. The CamAlien system essentially is a camera mounted on a moving car. The system continuously takes photos while driving, even at highway speeds. Images are anonymised for GDPR compliance and analysis is powered by the Pl@ntNet API, which returns species confidence scores. The software applies adaptive recording based on speed, and a human observer is able to tag plant sightings on the go, helping to build a better training set for the image recognition models. The species recognition algorithm (Pl@ntNet) returned a confidence value for each identification of a plant species in an image. A threshold value above which a confidence score is considered accurate was determined through expert validation and statistical modelling. Expert botanists annotated a subset of images with a range of confidence scores for presence/absence of the classified species. Presence/absence was then modelled as a binary response in a logistic GLM (General Linear Model) against confidence score. The confidence score providing a 50% probability of presence was then selected as threshold value. The images resulting from this filtering process were not all individually reviewed. Bram D'hondt 0000-0002-1330-1457 author Axel Neukermans 0000-0003-0272-9180 author Lars Dalby 0000-0002-7270-6999 principalInvestigator Mark A.K. Gillespie 0000-0001-9808-4836 author Kavi Askholm Mellerup author Toke T. Høye 0000-0001-5387-3284 principalInvestigator Sanne Govaert 0000-0002-8939-1305 publisher Biodiversa+ monitoring pilots aim to facilitate the harmonisation of biodiversity monitoring at a transnational level, by testing common protocols and new technologies. The Biodiversa+ IAS pilot project (2023-2027) implemented the monitoring of invasive alien plants using a vehicle-mounted camera system in various countries across Europe. Biodiversa+, the European Biodiversity Partnership supporting excellent research on biodiversity with an impact on policy and society. Biodiversa+ was jointly developed by BiodivERsA and the European Commission as part of the EU Biodiversity Strategy 2030. Europe During several field seasons of the project, the CamAlien camera system (AI Lab, Denmark) was mounted on vehicles to capture images of the vegetation along linear transport infrastructure. Typically, this system was mounted on a car for the monitoring of roadside vegetation. The system was also tested on other types of vehicles (e.g., boats, trains). Plant species were then identified from the images using species recognition algorithms (Pl@ntNet). The focus was specifically on invasive alien plants, in the context of large-scale surveys, rapid detection, and support of species management. This pilot study was conducted in 11 countries across Europe. 2026-01-27T10:52:19.015+00:00 dataset D'hondt B, Steen F, Neukermans A, Adriaens T, Dalby L, Gillespie M A, Mellerup K A, Høye T T, Bonnet P, Joly A, Govaert S (2026). IAS pilot - Observations of alien plant species using a vehicle-mounted camera system in Flanders (Belgium). Version 1.22. Test Organization. Occurrence dataset. https://ipt.gbif-test.org/resource?r=biodiversa_camalien_be&v=1.22 Dyrmann M, Skovsen SK, Christiansen PH et al. (2024) High-speed camera system for efficient monitoring of invasive plant species along roadways. F1000Research 2024, 13:360. https://doi.org/10.12688/f1000research.141992.2 Espitalier V, Lombardo JC, Goëau H, Botella C, Høye T T, Dyrmann M, Bonnet P, Joly A (2025). Adapting a global plant identification model to detect invasive alien plant species in high-resolution road side images. Ecological Informatics 89. https://doi.org/10.1016/j.ecoinf.2025.103129 https://www.biodiversa.eu/wp-content/uploads/2022/03/biodiversa_logo.svg 4dcca7f7-b67f-4215-9a8b-4f2fd38cc548/v1.22.xml