Imagine having a digital ear and eye that can automatically identify every bird song or plant type in a forest without a human needing to be there. It uses smart software and drones to map out nature reserves and track endangered species from the sky and ground. This makes keeping track of wildlife as easy as using a smart home security system for the wilderness.
Biodiversity monitoring is currently too slow, expensive, and fragmented because it relies on manual human sampling. This creates data gaps that make it difficult for land managers to comply with environmental laws.
AI-driven image and sound recognition software, high-resolution habitat maps using satellite and drone data, and a virtual lab for automated data processing.
If you are an ecological survey firm dealing with high labor costs for manual field counts — this project developed AI image and sound recognition that automates species identification. This reduces the need for constant human presence on-site while increasing data resolution.
If you are a land manager dealing with complex habitat compliance for nature reserves — this project developed drone and satellite mapping tools that provide high spatial resolution habitat condition metrics. This allows for faster, more accurate reporting of land health.
If you are a software provider dealing with fragmented biodiversity data for government agencies — this project developed a virtual lab to automate data streams from sensors and remote sensing. This creates a standardized way to retrieve habitat metrics across different regions.
Based on available project data, specific pricing is not listed, but the project aims to provide cost-effective solutions for land managers and agencies.
Yes, the project includes a specific work package (WP6) dedicated to upscaling results and testing tools at demonstration sites across Europe.
Based on available project data, the specific licensing model is not mentioned, though the goal is to make tools widely available to users and policy makers.
The tools are designed to support the EU biodiversity strategy 2030, the Birds and Habitats Directives, and the EU Nature Restoration Law.
The project focuses on the integration of new technology with existing research infrastructures and the creation of a virtual lab for automated workflow deployment.
The consortium is research-heavy with 8 academic or research entities (3 universities, 5 research centers) and a 20% industry presence consisting of 2 SMEs. This structure suggests the output is high-tech and scientifically validated, but the inclusion of SMEs indicates a push toward practical, usable tools rather than just theoretical papers.
Aarhus Universitet (DK)
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