Welcome to DeepSpatial 2021

Abstract:

The importance of spatial data science and Geo-AI is growing with the rise of spatial and spatiotemporal big data (e.g., trajectories, remote-sensing images, census and geo-social media). Societal use cases include Agriculture ( global crop monitoring, precision agriculture), Location-based services (e.g., navigation, ride-sharing), Public Health (e.g., monitoring disease spread), Environment and Climate (change detection, land-cover classification), Smart Cities (e.g., mapping buildings), etc. Classical data science and AI (e.g., machine learning) often perform poorly when applied to spatial data sets because of the many reasons. First, spatial data is embedded in a continuous space and classical statistics (e.g., correlation) are not robust to the modifiable areal unit problem. Second, spatial data-items have extended footprints (e.g., line strings, polygons) and implicit relationships (e.g., distance, touch). Third, high cost of spurious patterns requires guardrails (e.g., statistical significance tests) to reduce false positives. Furthermore, spatial autocorrelation and variability violate the classical assumption of data samples being generated independently from identical distributions, which risk models that are either inaccurate or inconsistent with the data. Thus, new methods are needed to analyze spatial data. This talk surveys common and emerging methods for spatial classification and prediction (e.g., spatial autoregression, spatial variability aware neural networks), as well as techniques for discovering interesting, useful and non-trivial patterns such as hotspots (e.g., circular, linear, arbitrary shapes ), interactions (e.g., co-locations , cascade , tele-connections ), spatial outliers, and their spatio-temporal counterparts.

Speaker Bio:

Dr. Shashi Shekhar, a McKnight Distinguished University Professor at the University of Minnesota and an U.C. Berkeley alumnus, is a leading scholar of spatial computing and Geographic Information Systems (GIS). He is serving on the Computing Research Association (CRA) board, and as a co-Editor-in-Chief of Geo-Informatica journal (Springer). Earlier, he served as the President of the University Consortium for GIS (UCGIS), and on many National Academies' committees. Recognitions include IEEE-CS Technical Achievement Award, UCGIS Education Award, IEEE Fellow and AAAS Fellow. Contributions include algorithms for evacuation route planning and spatial pattern (e.g., colocation, linear hotspots) mining, an Encyclopedia of GIS, a Spatial Databases textbook, and a spatial computing book for professionals.

Keynote 2: Graph Convolutional Neural Networks for Physics-informed AI Models

Abstract:

Deep learning has achieved significant successes in prediction performance by learning latent representations from data-rich applications, but we are confronted with many challenging learning scenarios in modeling natural phenomena, where a limited number of labeled examples are available or there is much noise in the data. Furthermore, there could be constant changes in data distributions (e.g. dynamic systems). Therefore, there is a pressing need to develop new generation deeper and robust learning models that can address these challenging learning scenarios. In this talk, I will discuss our recent work on differential graph neural networks for physics-Informed AI models via meta-learning and causal inference.

Speaker Bio:

Dr. Yan Liu is a Professor in the Computer Science Department and the Director of Machine Learning Center at University of Southern California. She was a Research Staff Member at IBM Research in 2006-2010 and Chief Scientist in Didi Chuxing in 2018. She received her Ph.D. degree from Carnegie Mellon University. Her research interest is machine learning and its applications to health care, sustainability and social network analysis. She has received several awards, including ACM Distinguished Member, NSF CAREER Award, Okawa Foundation Research Award, New Voices of Academies of Science, Engineering, and Medicine, Biocom Catalyst Award Winner, and ACM Dissertation Award Honorable Mention.