説明
データ レコード
この オカレンス（観察データと標本) リソース内のデータは、1 つまたは複数のデータ テーブルとして生物多様性データを共有するための標準化された形式であるダーウィン コア アーカイブ (DwC-A) として公開されています。 コア データ テーブルには、13,880 レコードが含まれています。
拡張データ テーブルは1 件存在しています。拡張レコードは、コアのレコードについての追加情報を提供するものです。 各拡張データ テーブル内のレコード数を以下に示します。
この IPT はデータをアーカイブし、データ リポジトリとして機能します。データとリソースのメタデータは、 ダウンロード セクションからダウンロードできます。 バージョン テーブルから公開可能な他のバージョンを閲覧でき、リソースに加えられた変更を知ることができます。
バージョン
次の表は、公にアクセス可能な公開バージョンのリソースのみ表示しています。
引用方法
研究者はこの研究内容を以下のように引用する必要があります。:
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 1.1. Research Institute for Nature and Forest (INBO). Occurrence dataset. https://ipt.inbo.be/resource?r=ias-pilot-be&v=1.1
権利
研究者は権利に関する下記ステートメントを尊重する必要があります。:
パブリッシャーとライセンス保持者権利者は Research Institute for Nature and Forest (INBO)。 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.
GBIF登録
このリソースをはGBIF と登録されており GBIF UUID: 9c85f295-cc92-473c-969a-bb5d3eccfd0bが割り当てられています。 Belgian Biodiversity Platform によって承認されたデータ パブリッシャーとして GBIF に登録されているResearch Institute for Nature and Forest (INBO) が、このリソースをパブリッシュしました。
キーワード
Occurrence; CamAlien; invasive species; alien species; non-native species; IAS; plants
連絡先
- メタデータ提供者 ●
- 最初のデータ採集者 ●
- 連絡先
https://orcid.org/0000-0002-1330-1457
- 最初のデータ採集者
- 最初のデータ採集者
- 最初のデータ採集者
- 最初のデータ採集者
- メタデータ提供者 ●
- 最初のデータ採集者
地理的範囲
Flanders, Belgium
| 座標（緯度経度） | 南 西 [50.705, 4.216], 北 東 [51.072, 4.983] |
|---|
生物分類学的範囲
6 non-native species of vascular plants
| Genus | Reynoutria sp. |
|---|---|
| Species | Ailanthus altissima, Impatiens glandulifera, Robinia pseudoacacia, Rosa rugosa |
時間的範囲
| 開始日 / 終了日 | 2024-07-18 / 2024-09-09 |
|---|
プロジェクトデータ
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.
| タイトル | Monitoring Invasive Alien Species with image-based methods |
|---|---|
| 識別子 | IAS pilot |
| ファンデイング | 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. |
| Study Area Description | Europe |
| 研究の意図、目的、背景など（デザイン） | 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. |
プロジェクトに携わる要員:
- 論文著者
収集方法
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.
| Study Extent | 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. |
|---|---|
| Quality Control | 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. |
Method step description:
- 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.
書誌情報の引用
- 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
追加のメタデータ
| 代替識別子 | 9c85f295-cc92-473c-969a-bb5d3eccfd0b |
|---|---|
| https://ipt.inbo.be/resource?r=ias-pilot-be |