Abstract

To test the feasibility of the deployment of Unmanned Ground Vehicles (UGVs) for automated intelligent patrol, detection, wildlife monitoring, identification across the national parks and reserves in Kenya.

Introduction

Wildlife tourism is a significant and growing contributor to the economic and social development in the African region through revenue generation, infrastructure development and job creation. According to a recent press release by the World Travel and Tourism Council [1], travel and tourism contributed $194.2
billion (8.5% of GDP) to the African region in 2018 and supported 24.3 million jobs (6.7% of total employment). Globally, travel and tourism is a $7.6 trillion industry, and is responsible for an estimated 292 million jobs [2]. Tourism is also one of the few sectors in which female labor participation is already above parity, with women accounting for up to 70% of the workforce [2].

However, the wildlife tourism industry in Africa is being increasingly threatened by rising human population and wildlife crime. As poaching becomes more organised and livestock incursions become frequent occurrences, shortages in ranger workforce and shortcomings in technological developments in this space put thousands of species at risk of endangerment, and threaten to collapse the wildlife tourism industry and ecosystem. According to The National Wildlife Conservation Status Report, 2015 – 2017, presented by the Ministry of Tourism and Wildlife of Kenya [3], there is currently a shortage of 1038 rangers, from the required 2484 rangers in Kenyan national parks and reserves, a deficit of over 40%. With tourism in Kenya contributing a revenue of $1.5 billion in 2018 [4], and with the wildlife conservancies in Kenya supporting over 700,000 community livelihoods [3], the recession of the wildlife tourism industry could have major adverse economic and social impacts on the country. It is thus critical that sustainable solutions are reached to save the wildlife tourism industry, and further research is fuelled in this area.

The national parks, reserves and conservancies in Kenya span thousands of square kilometers and make it difficult for rangers to track down all possible poaching activities. Poachers normally use guns, snares, and poison to capture wild animals. By collecting real world data on poaching activities, better learning of adversarial behavior is achieved and optimal strategies for anti-poaching patrols can be employed [5]. According to [5], a large number of security games research lacks actual adversary data and does not consider heterogeneity among large populations of adversary which makes it difficult to build accurate models of adversary behavior. Other problems inherent in past predictive models neglect the uncertainty in crime data, use coarse-grained crime analysis and propose time-consuming techniques that cannot be directly integrated into low-resource outposts [6].

To address shortages in ranger workforce, carry out monitoring activities more effectively, and detect criminal or endangering activities with greater precision, we propose the development of an open dataset containing georeferenced data on poaching incidents from the past 10 years as well as historical data on tagged elephant and rhino movements. We aim to observe correlations between the data using machine learning models and effectively model poaching trends and behavioural patterns to predict the location of the next poaching attack and suggest better patrol routes. The study will be carried out over a period of 4 months at Nairobi National Park in Kenya which covers a total area of 117 square kilometers and hosts many of the endangered wildlife species listed in the IUCN Red List of Threatened Species, such as the African Elephant and Black Rhinoceros.

Objectives

1. To generate a real world dataset that maps poaching activities within the park.
2. To develop a hybrid model that predicts the behavior of poachers by capturing their heterogeneity.
3. To improve the accuracy of the hybrid model by creating novel algorithms in determining poaching
   activities and hotspots.
4. To investigate the computation challenges faced when learning the behavioral model of poachers.

Vision

Our future vision is to test the feasibility of the deployment of Unmanned Ground Vehicles (UGVs) for automated intelligent patrol and wildlife monitoring across the national parks and reserves in Kenya. In
addition to carrying out automated patrol using the models learned in this study, the UGVs would be fitted
with an array of cameras and sensors that would enable it to navigate autonomously within the parks, and run multiple deep learning and computer vision algorithms that carry out numerous monitoring activities such as detection of poaching, livestock incursions, human wildlife conflict, distressed wildlife, and species
identification.

References

[1] “African tourism sector booming – second-fastest growth rate in the world”, WTTC press release , Mar. 13, 2019. Accessed on Jul. 11, 2019. [Online]. Available: https://www.wttc.org/about/media-centre/press-releases/press-releases/2019/african-tourism-sector-booming-second-fastest-growth-rate-in-the-world/

[2] “Supporting Sustainable Livelihoods through Wildlife Tourism”, World Bank Group , 2018.

[3] “The National Wildlife Conservation Status Report, 2015 – 2017”, pp. 75, 131, Ministry of Tourism and
Wildlife, Kenya , 2017.

[4] “Tourism Sector Performance Report – 2018” , Hon. Najib Balala, 2018.

[5] R. Yang, B. Ford, M. Tambe, and A. Lemieux, “Adaptive resource allocation for wildlife protection against illegal poachers,” in Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems , May 2014, pp. 453-460.

[6] S. Gholami, S. McCarthy, B. Dilkina, A. Plumptre, M. Tambe, et. al., “Adversary models account for
imperfect crime data: Forecasting and planning against real-world poachers,” in Proceedings of the 17th
International Conference on Autonomous Agents and MultiAgent Systems , July 2018, pp. 823-831.