In order to join virtual sessions, you must be registered and logged-in(Were you registered for the in-person meeting in Denver? if yes, just log in.) 
Note: All session times are in Mountain Daylight Time.

GeoAI Symposium: Deep Learning of Geospatial Patterns & Applications I

Type: Paper
Theme:
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Spatial Analysis and Modeling Specialty Group, Cyberinfrastructure Specialty Group
Organizers: Rui Zhu, Kevin Mwenda
Chairs: Guofeng Cao

Description

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?


Agenda

Type Details Minutes Start Time
Presenter Guofeng Cao*, Texas Tech University, A Deep Learning-Based Geostatistical Framework for Geospatial Data Analysis and Modeling 15 12:00 AM
Presenter Leila Character*, University of Texas - Austin, Tim Beach, University of Texas at Austin, Cody Schank, University of Texas at Austin, Lidar-Based Machine-Learning Model to Identify Archaeological and Natural Features in the Maya Lowlands of Belize, Guatemala, and Mexico 15 12:00 AM
Presenter Andong Ma*, Texas A&M University, Anthony M. Filippi, Texas A&M University, 3D LiDAR Point-Cloud Classification using Deep Learning 15 12:00 AM
Presenter Ling Cai*, University of California, TrafficTransformer: Capturing the Continuity and Periodicity of time 15 12:00 AM

To access contact information login