Scalable Camera Trap Data Analytics
Scalable AI species identification in the Republic of Congo
The Problem
WCS Congo deployed camera traps on over 3.4 million hectares of forest in the Republic of the Congo. This generated an enormous volume of data in HD video format that required species ID, animal counts, animal behaviour at 2 second intervals and distance measurements between the camera and the animal.
Large volumes of video data from camera traps can be difficult to manage
AI can be used to identify species, but results need to be checked by experts
Behaviour and distance to camera were needed for statistical modeling
Deadlines were short due to funding requirements
Our Solution
Okala applied AI with human-in-the-loop verification to identify and count mammals and birds. A custom interface was used to rapidly label behaviours and to measure animal distances.
Okala's Biodiversity Dashboard made it easy to validate AI output
Our experience allowed us to build a new interface to annotate animal behaviour in a short timeframe
We developed a new, robust algorithm to measure distances to animals
All data could be validated and checked by WCS scientists
This was an ambitious project where we could put AI to the test at very large scales. Okala's data platform allowed us to verify AI outputs and rapidly interact with the data using the intuitive interface.
Vittoria Estienne
Conservation Scientist
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