Remote sensing has become the most powerful tool to collect wildlife conservation data, such as information pertaining to habitat, distribution, and population trends. Monitoring methods such as camera traps, acoustic sensors, and aerial imagery are the most commonly used tools to generate rich datasets for varied applications. 

Data intervention can help complement conservation efforts immensely, such as by guiding action on decisions as to how to connect two cities via a roadway with minimum harm to wildlife habitat or where to deploy anti-poaching resources. Such meaningful conclusions, however, can only be arrived at after duly processing the data and deriving meaningful insights from it. This is, of course, done by passing the data through powerful AI algorithms specially designed for this purpose.

The big challenge that organisations face, in their pursuit of their conservation efforts, is in the stage that precedes data processing – the colossal task of data annotation. It involves the labelling the contents of the data to make it ready to be fed to AI and ML algorithms. Notably, wildlife data collection is a long drawn out exercise as data points often need to be collected from remote locations and tough terrains, over long periods. In addition, all existing methods require tremendous effort to be put into data annotation. These efforts can sometimes span months, especially when carried out by small teams at local NGOs. The effectiveness of the output takes a hit due to this delay, sometimes even rendering the whole exercise irrelevant due to outdated datasets.

AI can play an important role in automating, and hence accelerating, the data annotation, enabling real-time intervention and decision making (more on this here). Machine learning algorithms such as Deep Neural Networks, Convolutional Neural Networks etc. can be deployed to detect useful animal activity in the available acoustic and visual data.

Some of these early models can be deployed in production workflows to rapidly iterate and improve performance. These efforts, when used in combination with other models that do not require prior labelled data, can greatly escalate the efficiency of wildlife conservation efforts.

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