Machine Learning and the Fight Against Wildlife Poaching
Kenya as a country is highly dependent on tourism. Knows for its Safaris and serene beaches. However, poaching is a significant threat not only to the animals but the livelihood of many a citizen in the country. The capital city, Nairobi is in the unique position of being one of the only cities in the world with a Protected National Park within it’s boundaries. The park can be found towards the southern edge of the city, with a boundary fence running round three of its four edges, north, east and west. As such, the city has a significant role to play in the fight against Poaching.
One policy that the city could adopt in this fight against Poaching could be the use of machine learning in tracking wildlife populations. Google recently unveiled how their machine learning tools could be used to help protect endangered sea cows¹. The park plays host to several endangered species like the Black Rhino.
The adoption of the machine learning tools would help not only to protect the animals but would also be applicable in other areas of the park. These areas could include: managing visitors — the park receives on average 120,000 visitors each year; managing other administrative aspects of the park such as cleaning the park and Ranger deployment and response planning to poachers.
Of particular importance is response planning. By building effective capacity in responding to the epidemic of poaching, the city would inadvertently be building its response planning to crimes and other public safety events which would have been invaluable during the numerous terrorist attacks that have plagued the country.
If the city is not able to get these tools on its own, it could consider a public-private partnership with firms such as Google who are always looking for opportunities to improve their technologies.