TY  - JOUR
SN  - 2367-7163
KW  - camera
KW  -  computer vision
KW  -  statistics
KW  -  autonomous
KW  -  standards
KW  -  insects
KW  -  monitoring
KW  - 
collaboration
KW  -  inclusive
KW  -  COST
A1  - August, Tom
A1  - Balzan, Mario
A1  - Bodesheim, Paul
A1  - Brehm, Gunnar
A1  - Cantú-Salazar, Lisette
A1  - Castro, Sílvia
A1  - Chipperfield, Joseph
A1  - Ghisbain, Guillaume
A1  - Gomez-Segura, Alba
A1  - Goulnik, Jérémie
A1  - Groom, Quentin
A1  - Hogeweg, Laurens
A1  - Huijbers, Chantal
A1  - Kamilaris, Andreas
A1  - Kazlauskis, Karolis
A1  - Koch, Wouter
A1  - Korsch, Dimitri
A1  - Loureiro, João
A1  - Martin, Youri
A1  - Martinou, Angeliki
A1  - McFarland, Kent
A1  - Mestdagh, Xavier
A1  - Michez, Denis
A1  - Outhwaite, Charlie
A1  - Pegoraro, Luca
A1  - Pernat, Nadja
A1  - Pettersson, Lars
A1  - Pipek, Pavel
A1  - Preda, Cristina
A1  - Rolnick, David
A1  - Roth, Tobias
A1  - Roy, David
A1  - Roy, Helen
A1  - Runnel, Veljo
A1  - Sasic, Martina
A1  - Schigel, Dmitry
A1  - Sheard, Julie
A1  - Svenningsen, Cecilie
A1  - Teixeira, Heliana
A1  - Titeux, Nicolas
A1  - Tscheulin, Thomas
A1  - Tzirkalli, Elli
A1  - van der Velde, Marijn
A1  - van Klink, Roel
A1  - Vereecken, Nicolas
A1  - Vray, Sarah
A1  - Høye, Toke Thomas
AV  - public
ID  - discovery10204569
VL  - 11
JF  - Research Ideas and Outcomes
N1  - This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
TI  - Using Image-based AI for insect monitoring and conservation - InsectAI COST Action
N2  - The InsectAI COST action will support insect monitoring and conservation at the national and continental scale in order to understand and counteract widespread insect declines. The Action will bring together a critical mass of researchers and stakeholders in image-based insect AI technologies to direct and drive the research agenda, build research capacity across Europe and support innovation and application.


          There is mounting evidence that populations of insects around the world are in sharp decline. Understanding trends in species and their drivers is key to knowing the size of the challenge, its causes and how to address it. To identify solutions that lead to sustainable biodiversity alongside economic prosperity, insect monitoring should be efficient and provide standardised and frequently updated status indicators to guide conservation actions.


          The EU Biodiversity Strategy 2030 identifies the critical challenge of delivering standardised information about the state of nature and image-based insect AI can contribute to this. Specifically, the EU Nature Restoration Law will likely set binding targets for the high resolution data that cameras can provide. Thus, outputs of the Action will contribute directly to EU policies implementation, where biodiversity monitoring is considered a key component.


          The InsectAI COST Action will organise workshops, conferences, short-term scientific missions, hackathons, design-sprints and much more, across four Working Groups. These groups will address how image-based insect AI technologies can best address Societal Needs, support innovation in Image Collection hardware, create standardised approaches for Image Processing and develop novel Data Analysis and Integration methods for turning data into actionable insights.
PB  - Pensoft Publishers
UR  - https://doi.org/10.3897/rio.10.e134825
Y1  - 2025/02/10/
ER  -