Source of photo: Kostas Papafitsoros
Authors: Lukáš Adam and Lukáš Picek
AI is a useful tool especially when it comes to data analysis. AI-based algorithms can process large amounts of data and spot patterns or anomalies thanks to neural networks that have a more powerful processor than the human brain. This capacity is most often used for language models (used e.g. in the popular ChatGPT) and image processing. The latter in the context of ecology is our topic.
Image data originates from a number of sources. What are some examples? Satellite images are used to analyze changes in forest cover. Data on the loss of vegetation or changes in its color help conservationists detect loss of biodiversity. And our personal favorite – even wildlife can be monitored through the analysis of photos taken by drones, camera traps or ordinary cameras.
AI-powered wildlife monitoring
One of the uses of AI for wildlife monitoring is so-called re-identification, a term used for recognition of individuals. This field has gained importance in recent years due to the critical need for effective wildlife conservation and management strategies. Re-identification can provide important data on population size and population dynamics, such as birth and death rates, which are critical to understanding population health and developing conservation strategies. By re-identifying individual animals, scientists can also study movement patterns or help develop and evaluate conservation interventions.
Wildlife re-identification has traditionally relied on tracking technologies placed directly on the animals. For example, GPS collars can monitor the movement patterns of a group of animals over time and reveal their home ranges, migration routes and habitat preferences. Although these methods are effective, they are laborious, time-consuming, and handling of animals can be stressful for them.
Another approach is the use of genetic techniques which play a vital role in the re-identification of wildlife. Researchers can take samples of their feces or fur to analyze the DNA. This allows accurate re-identification of individuals without physically capturing or handling them. The key disadvantage? High price.
Gentle camera traps instead of collars or DNA samples
A suitable approach to re-identification is the use of non-invasive methods such as camera traps. These strategically placed cameras are activated by movement and capture images of animals in their natural habitat. These images can later be analyzed to identify individual animals based on distinguishing unique marks such as coat patterns, scars or other physical characteristics. Traditionally, these photos are analyzed manually. In recent years, AI has emerged as a powerful tool in this process by enabling more efficient and accurate identification of individual animals from large datasets.
There are several research groups in the Czech Republic that are interested in the application of AI to wildlife monitoring. Let’s look at two of them – one is located in our AI Center at CTU, the other at the Faculty of Applied Sciences of the University of West Bohemia in Pilsen (FAV ZČU).