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 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 have put thousands of species at risk of endangerment, and threaten to collapse the wildlife tourism industry and ecosystem.

Tourism in Kenya contributed a revenue of $1.5 billion in 2018 [3]. And The National Wildlife Conservation Status Report, 2015 – 2017 [4] presented by the Ministry of Tourism and Wildlife of Kenya claimed that wildlife conservancies in Kenya supported over 700,000 community livelihoods. The recession of the wildlife tourism industry could therefore 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.

Problem definition

According to The National Wildlife Conservation Status Report, 2015 – 2017 [4] presented by the Ministry of Tourism and Wildlife of Kenya, there is currently a shortage of 1038 rangers, from the required 2484 rangers in Kenyan national parks and reserves, a deficit of over 40%. To address shortages in ranger workforce, carry out monitoring activities more effectively, and detect criminal or endangering activities with greater precision, we propose the deployment of Unmanned Ground Vehicles (UGVs) for intelligent patrol and wildlife monitoring across the national parks and reserves in Kenya.

The UGVs would be fitted with a suite of cameras and sensors that would enable it to navigate autonomously within the parks, and run multiple deep learning and computer vision algorithms that can carry out numerous monitoring activities such as detection of poaching, livestock incursions, human wildlife conflict, distressed wildlife, and species identification.

The UGVs could be monitored from a central surveillance system, where alerts can be generated on detection of any alarming activity, and rangers dispatched to respond. Ethical considerations can be made to facilitate the deployment of these UGVs in a manner that aids the ranger workforce in their routine surveillance tasks throughout the national parks and reserves that often span thousands of square kilometers, rather than replace them. Sustainable and ethical automation could help create more jobs in the automotive and technology sectors without replacing current jobs.

The deployment of a project of this scale, however, would require significant investments in building the UGV, and require feasibility studies from the government and international wildlife conservation bodies. Furthermore, without reasonable computer vision and autonomous navigation accuracies, investments towards building the unmanned vehicle would be futile. It is thus crucial that efforts are first made towards solving the computer vision and autonomous navigation challenges posed by the rough terrains prevalent in national parks and reserves.

This project therefore serves as a stepping-stone towards adopting autonomous vehicle technology in Africa and pioneering further research in the field and its applications to broader areas beyond just transportation. Additionally, its adaptation in national park environments would allow it to be tested in unstructured environments lacking road infrastructure and free of traffic and pedestrians, thus allowing the systems to be tested safely and get quicker policy approvals. The scope of this research is hence limited to developing an end-to-end deep learning model that can autonomously navigate a vehicle over dirt roads and challenging terrain that is present in national parks and reserves.

The model will be trained on trail video as well as driving data such as steering wheel angle, speed, acceleration, and Inertial Measurement Unit (IMU) data. The accuracy of the model will be measured by calculating the error rate between the model’s prediction and the driver’s actual inputs over a given distance. We also look to publish the dataset of annotated driving data from national parks and reserves, the first of its kind, to encourage further research in this space. Additionally, we shall collect metadata such as number of patrol vehicles per square kilometer, average distance travelled per vehicle per day, distance of traversable road in the park per square kilometer, that can be used to give a preliminary analysis on the feasibility of the project results towards automated wildlife patrol.

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] “Tourism Sector Performance Report – 2018”, Hon. Najib Balala, 2018.
[4] “The National Wildlife Conservation Status Report, 2015 – 2017”, pp. 131, 74, 75 Ministry of Tourism and Wildlife, Kenya, 2017.