In the past decade, deep learning has become one of the most successful techniques in studying patterns thanks to the increasing power of modern computations. Multiple scientific domains such as computer vision, text mining and speech recognition significantly benefit from the advent of deep learning. Geographers also start to embrace the deep learning in analyzing geospatial patterns, example geospatial applications include object detection in remotely sensed images, trajectory and network analysis, geospatial semantic analysis, and so on. However, the nature of geospatial patterns is distinguishable from general patterns. Therefore, specifications and/or adaptations on applying deep learning are necessary to tackle geospatial problems. In addition, geographers also endeavor to contribute to deep learning by introducing techniques from geographic information sciences such as geostatistics, spatial optimization and spatial cognition. Specifically, researchers are interested in answering fundamental questions like: How can deep learning be employed and adapted to analyze and predict geographic phenomena (e.g. urban expansion & evolution)? What are the conceptual and fundamental considerations behind building and training neural networks to conduct geospatial data analysis? What are the mechanics underlying deep learning methods and how can these techniques be leveraged by geographers to uncover patterns and structure embedded in geographic data? How can theories and/or techniques (e.g., spatial statistics, spatial optimization) developed in GIScience be adapted to improve the performance of deep learning?
To advance this research direction, we invite researchers to present their work that are relevant to topics that include, but are not limited to:
(1) Methodologies to improve the performance of deep learning of geospatial patterns
(2) Sensitivity analysis of deep learning on understanding geospatial patterns
(3) Applications of deep learning from remotely sensed images
(4) Applications of deep learning of geospatial networks/trajectories
(6) Using deep learning to analyze and predict urban land use/land cover (LULC) changes
Session organizers: Guofeng Cao (firstname.lastname@example.org), Rui Zhu (email@example.com), and Kevin Mwenda (firstname.lastname@example.org).
|Presenter||Wenxuan Liu*, Wuhan University, Yi Fang, Santa Clara University, Huayi Wu, Wuhan University, Variational Autoencoder for High-resolution Remote Sensing image Retrieval||20||10:00 AM|
|Presenter||Guofeng Cao*, Texas Tech University, A Deep Learning-Based Geostatistical Framework for Geospatial Data Analysis and Modeling||20||10:20 AM|
|Presenter||Xi Liu*, Pennsylvania State University, Using Deep Learning to Quantify Urban Residential Interiors||20||10:40 AM|
|Presenter||Yang Zhong*, Claremont Graduate University, Shaohua Wang, University of California, Santa Barbara, Erqi Wang, SuperMap Software Co., Ltd, Shaojun Li, SuperMap Software Co., Ltd, Wenwen Cai, SuperMap Software Co., Ltd, Hao Lu, SuperMap Software Co., Ltd, Towards Integrated GIS Platform Architecture for Spatiotemporal Big Data||20||11:00 AM|
|Presenter||Mark Coletti*, Oak Ridge National Laboratory, Jeanette Weaver, Oak Ridge National Laboratory, Anne Berres, Oak Ridge National Laboratory, Lexie Yang, Oak Ridge National Laboratory, Dalton Lunga, Oak Ridge National Laboratory, Jibonananda Sanyal, Oak Ridge National Laboratory, Results of using an evolutionary algorithm to optimize a building footprint detecting deep learner's hyper-parameters||20||11:20 AM|
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