Detecting Hotspots of Human-Wildlife Conflicts in India using News Articles and Aerial Images

Human-wildlife conflict (HWC) is one of the most pressing conservation issues at present, with incidents leading to human injury and death, crop and property damage, and livestock predation. Since acquiring real-time data and performing manual analysis on those incidents are costly, we propose to leverage machine learning techniques to build an automated pipeline to construct an HWC knowledge base from historical news articles. Our unsupervised and active learning methods are not only able to recognize the major causes of HWC such as construction, pollution, and farming, but can also classify an unseen news article into its major cause with 90% accuracy. Moreover, our interactive visualizations of the knowledge base illustrate the spatial and temporal trend of human-wildlife conflicts across India for index by cities and animals. Based on our findings that most conflict zones include areas where human settlements are near forested areas, we extend our study to include satellite imagery to identify such proximity zones. We conduct a case study to use this method to identify human-elephant conflict hotspots in northern and western parts of the Indian state of West Bengal. We expect that our findings can inform the public of HWC hotspots and help in much more informed policymaking.