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AI for Nature at Large Scale and High Resolution
AI can help fill biodiversity knowledge shortfalls only if biological knowledge is built into the models and the data infrastructure is FAIR for AI and returns value to primary data collectors, because 'there is no AI without data'.
Tanya Berger-Wolf ↗
Mainstreaming high-throughput biodiversity monitoring using AI
AI identification platforms like ARISE can take sensor data automatically to GBIF-published, standardised data for protected-area managers, but training costs are high and human verification still matters.
Niels Raes ↗
Cross-Modal Coordination for Biodiversity Monitoring at Scale: Lessons from SmartWilds
Coordinating multiple sensor modalities with AI at the far edge, informed by synchronised multimodal datasets like SmartWilds, can scale and adapt biodiversity monitoring.
Jenna Kline ↗
Scalable Edge AI for Real-Time Biodiversity Monitoring: tracking invasive plant species in roadside imagery
Distilling a tiling-based plant identifier into a single ConvNeXt model makes high-resolution roadside invasive-species monitoring fast, cheap and more accurate.
Giulio Martellucci ↗
Soundscape China: Listening to the Sounds of Nature with AI
Soundscape China is building a nationwide acoustic sensor network, database and AI models, gathering 3.6 million recordings in its first year.
Congtian Lin ↗
Camtrap DP: extending data standard designed for camera trapping research to support AI workflows and new data sources
Camtrap DP fits data from any stationary sensor, and converging community feedback shows how to extend it for AI provenance and multimodal monitoring.
Karolina Kuczkowska ↗
Digital Twins as System-Optimization Tools: Provisioning Edge and Cloud Infrastructure for Biodiversity Monitoring at Scale
Digital twins should close the sim-to-real loop for the sensor systems too, letting teams plan where and how to deploy multimodal monitoring before going into the field.
Tanya Berger-Wolf ↗
FAIR² Drones: An AI-Ready Standard for Cross-Domain Wildlife Drone Dataset
A shared dataset card that captures ecological, robotic and computer vision metadata makes costly drone datasets reusable across disciplines.
Jenna Kline ↗
From Standardized Field Methods to AI-Ready Knowledge: The LEPMON Project with the LAUP Infrastructure Stack for Automated Moth Monitoring
LEPMON's LAUP portal shows an end-to-end infrastructure for camera-based moth monitoring that builds Camtrap DP into routine data management, making millions of observations downloadable and analysable.
Peter Grobe ↗
Standardizing Multimodal Insect Monitoring Data for AI-Ready Pipelines: Lessons from an InsectAI Community Datathon
Camtrap DP can largely accommodate automated insect monitoring data today, but the community needs agreed guidance and a few targeted extensions (track IDs, parent media and crop offsets, target taxonomic scope, identification provenance) to handle insect-specific challenges.
Jamie Alison ↗