Augmenting deep land use prediction with randomized simulation. (7th July 2022) - Chen, Liyan; Chen, Zhangwu; Lin, Lianhui; Ye, Qi; Guo, Shihui; Lin, Juncong
Abstract: Land use information is the basis of various geo‐spatial applications. Traditionally, land use patterns are predicted with agent‐based simulation, suffering from a long convergence process. Deep learning techniques have recently been used for land use classification but not prediction, due to the lack and difficulty of collecting enough training data. This paper proposes a novel paradigm for land use data generation with a randomized simulation strategy. We also design a tailored deep land use prediction model, LUPnet, to demonstrate the usage of the paradigm. Experimental results reveal the effectiveness of our method. Abstract : Randomized Land Use Simulation, an effective scheme to assist the training of land use prediction models.