Recent years have witnessed significant advancements in deep learning, machine learning, as well as artificial intelligence (AI) in general. The new methods and techniques brought by these advancements are transforming geospatial research in a variety of areas. For example, recent studies have shown deep learning techniques coupled with volunteered geographic information (such as OpenStreetMap data) can accurately extract buildings from satellite images for humanitarian mapping. Artificial intelligence methods are also enabling self-driving cars and intelligent transport system by analyzing large amounts of geographic information gathered by traffic cameras and sensors in real time. Many deep learning and machine learning techniques have facilitated natural language processing and have helped discover new knowledge from (geotagged)natural language texts. There also exist many other applications of deep learning and machine learning in geospatial research, such as spatial diffusion prediction in epidemiology, urban expansion analysis, and hyperspectral image analysis. In this context, we organize a special symposium focusing on the current status, recent advances, and possible future directions of this exciting research theme at the 2018 AAG Annual meeting, April 10-14, New Orleans, Louisiana. We aim to bring in geographers, GI scientists, spatial modeling experts, computer scientists, spatial data scientists, epidemiologists, urban planners, transportation professionals, and many others to discuss this rapidly developing research frontier.
|Presenter||Dawn J. Wright*, Esri, Thomas Maurer, Esri, Hua Wei, Esri, Toward Easy Export of Imagery Products and Feature Classes as Training Data for Deep Learning Frameworks||20||10:00 AM|
|Presenter||Shawn Newsam*, University of California, Merced, Yi Zhu, University of California, Merced, Xueqing Deng, University of California, Merced, Geographic Knowledge Discovery Using Deep Learning Applied to Ground-Level Images and Videos||20||10:20 AM|
|Presenter||Kenan Li*, University of Southern California, Nina Lam, Louisiana State University, Self-Taught Deep-Learning Convolutional Neural Network for Features Detection in Unlabeled Remote Sensing Images.||20||10:40 AM|
|Presenter||Karim Malik*, , Colin Robertson, Wilfrid Laurier University, Multiscale segmentation, spatial pattern analysis, and vegetation classification of heterogeneous landscapes in the Canadian Low Arctic Tundra||20||11:00 AM|
|Presenter||Wenwen Li*, Arizona State University, Chia-Yu Hsu, Arizona State University, Recognizing terrain features on terrestrial surface using a deep learning model||20||11:20 AM|
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