{"type":"ScholarlyArticle","title":"AI for Conservation: Aerial Monitoring to Learn and Plan against Illegal Actors","authors":[{"name":"Elizabeth Bondi","slug":"elizabeth-bondi","given":"Elizabeth","family":"Bondi","affiliation":"University of Southern California"}],"abstract":"Conservation of our planet’s natural resources is of the utmost importance and requires constant innovation. This project focuses on innovation for one aspect of conservation: the reduction of wildlife poaching. Park rangers patrol parks to decrease poaching by searching for poachers and animal snares left by poachers. Multiple strategies exist to aid in these patrols, including adversary behavior prediction and planning optimal ranger patrol strategies. These research efforts suffer from a key shortcoming: they fail to integrate real-time data, and rely on historical data collected during ranger patrols. With the recent advances in unmanned aerial vehicle (UAV) technology, UAVs have become viable tools to aid in park ranger patrols. There is now an opportunity to augment the input for these strategies in real time. Detection is done on real-time data collected from UAVs. Detection will then be used to learn adversaries’ behaviors, or where poaching may occur in the future, in future work. This will then be used to plan where to fly in the long term, such as the next mission. Finally, planning where to fly next during the current flight will depend on the long term plan and the real-time detections in case a poacher is spotted. Through our collaboration with Air Shepherd, a program of the Charles A. and Anne Morrow Lindbergh Foundation, we have already begun deploying poacher detection prototypes in Africa and will be able to deploy further advances there in the future.","doi":"10.24963/ijcai.2018/825","url":"https://ijcai.org/proceedings/2018/825","pdf":"https://ijcai.org/proceedings/2018/0825.pdf","markdown":"https://ijcai.org/proceedings/2018/825.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/825","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":825,"pages":{"first":5763,"last":5764},"keywords":["Multidisciplinary Topics and Applications: Computational Sustainability","Knowledge Representation, Reasoning, and Logic: Game Theory","Robotics and Vision: Vision and Perception"],"videos":[],"references":[{"position":1,"raw":"[Bondi et al., 2017] Elizabeth Bondi, Fei Fang, Debarun Kar, Venil Noronha, Donnabell Dmello, Milind Tambe, Arvind Iyer, and Robert Hannaford. Viola: Video label- ing application for security domains. In GameSec, 2017.","title":"Viola: Video labeling application for security domains","authors":"Elizabeth Bondi, Fei Fang, Debarun Kar, Venil Noronha, Donnabell Dmello, Milind Tambe, Arvind Iyer, Robert Hannaford","year":"2017","venue":"GameSec","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bondi et al., 2018a] Elizabeth Bondi, Debadeepta Dey, Ashish Kapoor, Jim Piavis, Shital Shah, Fei Fang, Bistra Dilkina, Robert Hannaford, Arvind Iyer, Lucas Joppa, and Milind Tambe. Airsim-w: A simulation environment for wildlife conservation with uavs. In ACM COMPASS, 2018.","title":"Airsim-w: A simulation environment for wildlife conservation with uavs","authors":"Bondi","year":"2018","venue":"ACM COMPASS","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bondi et al., 2018b] Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe, and Ram Nevatia. Spot poachers in action: Aug- menting conservation drones with automatic detection in near real time. In IAAI, 2018.","title":"Spot poachers in action: Augmenting conservation drones with automatic detection in near real time","authors":"Bondi","year":"2018","venue":"IAAI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gholami et al., 2017] Shahrzad Gholami, Benjamin Ford, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Dri- ciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsub- aga, and Joshua Mabonga. Taking it for a test drive: A hybrid spatio-temporal model for wildlife poaching pre- diction evaluated through a controlled field test. In ECML PKDD, 2017.","title":"Taking it for a test drive: A hybrid spatio-temporal model for wildlife poaching prediction evaluated through a controlled field test","authors":"Shahrzad Gholami, Benjamin Ford, Fei Fang, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, Joshua Mabonga","year":"2017","venue":"ECML PKDD","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Nguyen et al., 2016] Thanh H Nguyen, Arunesh Sinha, Shahrzad Gholami, Andrew Plumptre, Lucas Joppa, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Rob Critchlow, et al. Capture: A new predic- tive anti-poaching tool for wildlife protection. In Proceed- ings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pages 767-775. Interna- tional Foundation for Autonomous Agents and Multiagent Systems, 2016.","title":"Capture: A new predictive anti-poaching tool for wildlife protection","authors":"Arunesh Thanh H Nguyen, Shahrzad Sinha, Andrew Gholami, Lucas Plumptre, Milind Joppa, Margaret Tambe, Fred Driciru, Aggrey Wanyama, Rob Rwetsiba, Critchlow","year":"2016","venue":"Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Ren et al., 2015] Shaoqing Ren, Kaiming He, Ross Gir- shick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In NIPS, pages 91-99, 2015.","title":"Faster r-cnn: Towards real-time object detection with region proposal networks","authors":"Ren","year":"2015","venue":"NIPS","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}