Abstract

Human-wildlife conflict (HWC) is one of the most pressing conservation issues of our time, 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 spatio and temporal trend of human wildlife conflicts across India for index by cities and animals. We hope that our findings can inform the public of HWC hostspots and help future policy making.

Visualization

Citation

Acknowledgements

The website template was borrowed from Michaël Gharbi.