Data-Driven Multimodal Patrol Planning for Anti-poaching


  • Weizhe Chen Shanghai Jiao Tong University
  • Weinan Zhang Shanghai Jiao Tong University
  • Duo Liu WWF-China NEC
  • Weiping Li WWF-China NEC
  • Xiaojun Shi Huang Ni He Forest Bureau
  • Fei Fang Carnegie Mellon University



Artificial Intelligence For Social Good, Computational Sustainability, Wildlife Conservation, Applied Machine Learning, Route Planning


Wildlife poaching is threatening key species that play important roles in the ecosystem. With historical ranger patrol records, it is possible to provide data-driven predictions of poaching threats and plan patrols to combat poaching. However, the patrollers often patrol in a multimodal way, which combines driving and walking. It is a tedious task for the domain experts to manually plan such a patrol and as a result, the planned patrol routes are often far from optimal. In this paper, we propose a data-driven approach for multimodal patrol planning. We first use machine learning models to predict the poaching threats and then use a novel mixed-integer linear programming-based algorithm to plan the patrol route. In a field test focusing on the machine learning prediction result at Jilin Huangnihe National Nature Reserve (HNHR) in December 2019, the rangers found 42 snares, which is significantly higher than the historical record. Our offline experiments show that the resulting multimodal patrol routes can improve the efficiency of patrol and thus they can serve as the basis for future deployment in the field.




How to Cite

Chen, W., Zhang, W., Liu, D., Li, W., Shi, X., & Fang, F. (2021). Data-Driven Multimodal Patrol Planning for Anti-poaching. Proceedings of the AAAI Conference on Artificial Intelligence, 35(17), 15270-15277.



IAAI Technical Track on Emerging Applications of AI