Purdue-Chen-MC2
Ashley Yang - West Lafayette Jr./Sr. High School, West Lafayette, United States
Hao Wang - Purdue University, WEST LAFAYETTE, United States
Qianlai Yang - Northeastern University, Boston, United States
Qi Yang - Purdue University, West Lafayette, United States
Ziqian Gong - Purdue University, West Lafayette, United States
Zizun Zhou - Purdue University, West Lafayette, United States
Zhenyu Cheryl Qian - Purdue University, West Lafayette, United States
Yingjie Victor Chen - Purdue University, West Lafayette, United States
Room: Bayshore II
2024-10-13T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-13T12:30:00Z
Full Video
Abstract
The SunSpot project is a comprehensive solution to address the 2024 IEEE VAST Challenge MC2, focusing on detecting abnormal vessel activities. Our method integrated data on fishing records, vessel trajectories, commodity-vessel relationships, and fish distributions. We created a set of visualizations to help analysts better understand the characteristics of the area, vessels, and fishing activities. We considered a vessel’s departure from and return to a harbor as a basic cycle of activity and classified these cycles into patterns based on location and dwell time. By visualizing the spatial and temporal aspects of these cycles, we effectively distinguished illegal fishing from normal fishing activities. Our solution highlights the strengths of a multidirectional approach in data analytics, incorporating vessel information, fish origins, exported commodities, and shipping ports.