In today’s era of big data, advanced algorithms, and immense computational power, artificial intelligence (AI) is bringing tremendous opportunities and challenges to geospatial research. Big data enable computers to observe and learn the world from many angles, while high performance machines support the training development and application deployment of AI models within reasonable amount of time. Recent years have witnessed significant advances in the integration of geography and AI in both academia and industry, and the outcome is an exciting and interdisciplinary area-- GeoAI. There have already been many successful studies. Focusing on modeling the physical nature, a recent publication in PNAS has shown that deep learning can improve the representation of clouds that are smaller than the grid resolutions of climate models. Examining the human society, AI and natural language processing methods, such as word embeddings, are helping quantify changes in stereotypes and attitudes toward women and ethnic minorities over 100 years in the United States. There are also many other applications that effectively integrate AI with problems in geospatial studies, such as vehicle trajectory prediction, indoor navigation, historical map digitizing, gazetteer conflation, geographic feature extraction, geo-ontologies, and place understanding.
Building on the great success of the 1st symposium in AAG 2018, we are organizing the 2nd AAG Symposium on GeoAI and Deep Learning for Geospatial Research focusing on the current status, recent advances, and possible future directions of this exciting research theme at the 2019 AAG Annual meeting, April 3-7, Washington DC. We aim to bring together 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. In particular, we hope to provide a venue for researchers from all geospatial disciplines to start the dialog on how to fertilize this exciting field of GeoAI, how can we better prepare our students with essential knowledge and skills, and how can we foster cross-discipline collaborations.
|Presenter||Morteza Karimzadeh*, Purdue University, Luke Snyder, Purdue University, Yi-Shan Lin, Purdue Univeristy, David S. Ebert, Purdue University, Interactive Deep Learning for Identifying Relevant Social Media Posts in Crisis Monitoring||20||4:30 PM|
|Presenter||Shawn Newsam*, University of California, Merced, Deep Learning for Geographic Knowledge Discovery in Complex Spatial Data||20||4:50 PM|
|Presenter||Wenwen Li*, Arizona State University, A weakly supervised deep learning model for object detection of landform features||20||5:10 PM|
|Presenter||Benoit Parmentier*, National Socio-Environmental Synthesis Center, University of Maryland, Marco Millones, Geography, University of Mary Washington, Albert Decatur, Hachure, Hichem Omrani, Luxembourg Institute of Socio-Economic Research, Giuseppe Amatulli, Yale School of Forestry and Environmental Studies, Tijana Jovanovic, National Socio-Environmental Synthesis Center, University of Maryland, Neeti Neeti, Natural Resources, TERI University, Modeling land cover change in Houston with Deep Learning||20||5:30 PM|
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