Researchers at the University of Cambridge have developed an innovative approach to locating hedgehog habitats using artificial intelligence and satellite data. Since hedgehogs are difficult to spot from orbit, the team focused on identifying bramble thickets, which are typical environments for these animals.
This is reported by Business • Media
How the New AI Model Works
The analysis utilized data from Sentinel satellites, owned by the European Space Agency, combined with machine learning algorithms. The model integrates logistic regression, nearest neighbor classification, and the TESSERA system for processing satellite images. Additionally, data provided by citizens through the iNaturalist platform was taken into account.
“According to the scientists, this hybrid approach enabled the creation of a map of potential hedgehog habitats across Great Britain.”
Field trials conducted in Cambridge confirmed that the model reliably identifies large open bramble thickets. However, smaller bushes growing under trees were detected less accurately due to the limitations of satellite imagery. Despite this, the system has already demonstrated its effectiveness, particularly in the context of monitoring large areas.
Prospects for Technology Application
The new methodology, even at an early stage, shows potential for large-scale monitoring of hedgehog populations and other vulnerable species. Unlike traditional nighttime observations, which are labor-intensive, satellite analysis allows for the simultaneous assessment of large areas. This paves the way for more effective national conservation programs.
Scientists note that this is currently just a proof of concept, and the model has not yet undergone full scientific peer review. However, the team plans to expand testing, including implementing an active learning system for field conditions using mobile devices.
Experts believe that the developed approach could be beneficial not only for protecting hedgehogs. Similar algorithms can assist in monitoring invasive plants, agricultural pests, or changes in ecosystems. This project demonstrates how modern AI tools complement traditional methods of biodiversity conservation.