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.
|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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