Data Tools: AI and wildlife case study

2015 Case study: Unmanned Aerial Vehicles (UAVs) and Artificial Intelligences (AI) revolutionizing Wildlife Monitoring and Conservatism


Aiding in monitoring and conservationism of endangered or at risk of being endangered animals is at the heart of effective wildlife management.  Understanding the current population of animals is key.  However, current techniques like remote photography, camera traps, tagging, GPS collars, scat detect dogs, and DNA sampling is costly on the already strapped resources.  The authors in this study propose to use big data, AI, UAVs, and imagery to help effectively count the wildlife without depleting resources, disturbing the wildlife, improve safety, and improved statistical integrity.

The authors equipped a Mobius RGB camera with 1080p resolution and an FLIR Thermal Camera at 640×510 to an S800 EVO Hexacopter, which has three modes of travel, predefined flight mode via GPS, stabilized mode like autopilot, and manual.  The camera’s main goal is to capture footage of the area, split the image into a high contrast, identify patterns using AI and match them to the respective animal, and add the identified animal to the total count.  Using infrared cameras, the higher temperature animals sick out from the vegetation and soil background. Therefore a filter is applied to color the animal white and the background black to allow for classification and pattern recognition to occur.

Data Collection Procedures:

This idea was tested against the koala population given that they are iconic to Australia and are a vulnerable species.  The area that they studied was the Sunshine Coast, 57km north of Brisbane, Queensland, Australia, where the total ground truth number of koalas is 6. They flew on November 7, 2014, on 7:10-8:00 A.M. to allow for the largest temperature contrast between the koalas and background.  They flew at three different vertical levels: 20 m, 30 m, and 60 m.  A koala was identified if they were in 10 consecutive frames, didn’t make big jumps in locations within those frames, and that the size of the koala didn’t drastically increase.

Evaluation of effectiveness:

At each of the three levels, 100% of the koalas were identified.  However, it is important to note that there was a greater chance for a false positive at 60 m above ground surveillance and it took almost twice the time for the AI classification algorithm to detect the koalas.  The authors suggested that improving the AI classification algorithm by adding more template shapes for animals at different angles will help speed up the AI and improve the quality of detection.  Also, the quality of the templates can contribute to the quality of the detection.  This illustrates that there is a need to add more dynamic templates to the system, thus creating a bigger dataset to draw inferences from that can the higher the quality in detection.  Therefore, the combination of big data and AI is important for this study.

Other applications:

The benefit of this application of UAV, data analytics, and AI could be further extended to search and rescue missions for humans lost in national parks, etc.  The UAVs can supplement human and dog trackers, to gain an advantage of finding the victims quickly since time is extremely important.  Therefore, besides just for conservationist, park rangers can adapt these methods to help in recovery missions.  Another application could include the Department of Defense, for search and rescue missions, or mitigation of the casualties during times of war.


  • Gonzalez, L. F., Montes, G. A., Puig, E., Johnson, S., Mengersen, K., & Gaston, K. J. (2015). Unmanned Aerial Vehicles (UAVs) and Artificial Intelligences revolutionizing Wildlife Monitoring and Conservatism. Sensors 1(97). DOI: 10.3390/s16010097

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