May 2, 2019

Using AI to Hunt the Hunters

Despite the huge conservation efforts of multiple countries, organisations and individuals, four African elephants are killed every hour.

Apprehending animal poachers is a herculean task, especially given the lack of human and technological resources in such vast spaces.

Motion sensors, armed guards, and cameras can only do so much. For instance, sensors produce a lot of false flags from swaying branches and birds, while cameras require considerable power and sometimes fail to produce clear images.

Interestingly, as AI is increasingly utilised in a vast number of fields from medicine to art and music, it’s also being used to fight animal poaching.

The National Geographic Society and Leonardo DiCaprio Foundation are partnering with non-profit RESOLVE to deploy anti-poaching camera systems in a hundred national parks in Africa.

These smaller and more energy efficient devices can easily and reliably recognise and differentiate between humans, vehicles, and animals – helping rangers identify and arrest poachers before they kill more wildlife.

Larger protected areas where animals roam freely, however, are still challenging for cameras and rangers to watch over. To address these risks, an AI system called PAWS (Protection Assistant for Wildlife Security) has been developed to predict where poachers will strike before they even do so.

How PAWS works (Source: www.cais.usc.edu)

Created by computer scientists Milind Tambe and Fei Fang, PAWS uses a large amount of data from previous patrols to predict where poaching is most likely to occur, enabling rangers to deploy personnel to prioritised areas.

This predictive model is combined with a game theory model to help organise random and effective distribution of rangers, suggesting alternative patrol routes so that poachers do not know where they are. 

Computer scientist and PAWS creator Fei Fang (Image source)

The results from trials in Uganda are promising: in areas where PAWS predicted high probabilities of poaching activity, rangers found 10 times more activity than those predicted as low-risk.

These are great examples of how technology and human ingenuity can combine for good – providing hope for our fragile animal populations and building the case for further investment in AI research and development.


Al-Nouaman Mekouar

Tech Lead / Software Development

Nouaman is software builder and technologist who loves building tools that solve big and small problems. His current interests are computer vision, AI for health care, mobile apps and the fusion of arts & technology. In his free time, he enjoys playing piano, traveling, surfing, and hanging out with his 3 cats.


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