Artificial Intelligence and Deep Learning Symposium: AI for Spatial Optimization

Type: Paper
Sponsor Groups: Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group
Poster #:
Day: 4/13/2018
Start / End Time: 10:00 AM / 11:40 AM (MDT)
Room: Grand Ballroom A, Astor, 2nd Floor
Organizers: Kai Cao
Chairs: Kai Cao


Spatial optimization is a powerful spatial analysis technique that can be used to identify and exploit optimal solution(s) and generate a large number of alternatives. The formulation of such problems involves maximizing or minimizing one or more objectives while satisfying a number of constraints. Solution techniques often rely on exact models, such as linear programming and integer programming, or heuristic algorithms, such as Tabu Search, Simulated Annealing, and Genetic Algorithms. Spatial optimization techniques have been utilized in numerous planning applications, such as location-allocation modeling/site selection, land use planning, regionalization, routing and urban design. These methods can be seamlessly integrated into the planning process and generate many optimal/near-optimal planning scenarios or solutions, in order to more quantitatively and scientifically support the planning process. However, as most of such problems are NP-hard in nature, even a small dataset will generate a very complex solution space and therefore tend to be very computational intensive. In this context, alongside the quick development of artificial intelligence (AI) as well as the computing resources, the session of AI for Spatial Optimization from Artificial Intelligence and Deep Learning Symposium aims to capture the latest advancement and encourage more studies in this field.


Type Details Minutes Start Time
Presenter Guiming Zhang*, University of Wisconsin - Madison, A-Xing Zhu, University of Wisconsin-Madison, Representativeness-directed sample spatial bias mitigation for predictive mapping 20 10:00 AM
Presenter Kai Cao*, National University of Singapore, Calibrating a cellular automata model for understanding rural–urban land conversion: a Pareto front-based multi-objective optimization approach 20 10:20 AM
Presenter Dianfeng Liu*, , Xuesong Kong, School of Resource and Environmental Sciences, Wuhan University, Yasi Tian, School of Resource and Environmental Sciences, Wuhan University, Yaolin Liu, School of Resource and Environmental Sciences, Wuhan University, A social-connection-based particle swarm optimization algorithm for rural settlement reconstruction 20 10:40 AM
Presenter SEONG-AH CHO*, Seoul National University, Gunhak Lee, Seoul National University, Optimization of the foodtruck location by spatio-temporal aspect in Seoul 20 11:00 AM
Presenter Yulun Zhou*, The Chinese University of Hong Kong, Bo Huang, The Chinese University of Hong Kong, Spatial Land Use/Cover Optimization with Genetic Algorithm: Incorporating Spatial Heteorogeneity 20 11:20 AM

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