Using drones and AI for koala management

In ForestTECH, Issue23 by FIEA

In a paper first published in, the authors discuss an interesting applied technology use case, using both drone data collection and AI to allow for the more frequent and accurate detection of specific individual animals or species of animals.

The use case is for koalas, which in addition to being cute and cuddly are shy creatures. Koalas reside in forests with varying density, and are of interest to researchers. Prior to the current method described, teams of observers would go into areas to observe the number and density of koalas via physical inspection – just looking for them. The study finds that even experienced observers are only likely to find 70% of the koalas present in a given area; less experienced observers are only able to locate 23% of the koalas in a given area. This makes sense: many koalas look alike.

Also being relatively small, koalas are easy to miss; so the accurate data collection is more suspect. This issue has varying importance given the size and the area covered (think elephants or giraffes, which are easier to locate given their larger size, but may be in more remote areas further from where humans reside and therefore less accessible).

Now, drones equipped with thermal imaging can find the koalas – and AI can identify the specific animal, to avoid counting it twice and provide greater insight into behaviour and health.

From the abstract to the story:

“Effective wildlife management relies on the accurate and precise detection of individual animals. These can be challenging data to collect for many cryptic species, particularly those that live in complex structural environments. This study introduces a new automated method for detection using published object detection algorithms to detect their heat signatures in RPAS-derived thermal imaging.

As an initial case study, we used this new approach to detect koalas (Phascolarctus cinereus), and validated the approach using ground surveys of tracked radio-collared koalas in Petrie, Queensland. The automated method yielded a higher probability of detection (68–100%), higher precision (43–71%), lower root mean square error (RMSE), and lower mean absolute error (MAE) than manual assessment of the RPAS-derived thermal imagery in a comparable amount of time.

This new approach allows for more reliable, less invasive detection of koalas in their natural habitat. This new detection methodology has great potential to inform and improve management decisions for threatened species, and other difficult to survey species.”

The data collection is something that we are used to seeing with drones, but the combination of AI is interesting in this application. Interpreting the data manually is tedious and time-consuming, as well as error prone. A commercial application such as reviewing aerial photography from a drone for a long pipeline inspection is very analogous, or even CCTV camera’s looking for bad guys: looking at large amounts of video data is difficult, boring, and prone to errors.

It seems, however, that koalas have individual heat signatures. That allows scientists to identify individual animals with some confidence.

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