Symposium on Frontiers in Geospatial Data Science: Synergizing Geospatial Data Science with Domain Applications III: Remote Sensing

Type: Paper
Sponsor Groups: Cyberinfrastructure Specialty Group, Geographic Information Science and Systems Specialty Group, Remote Sensing Specialty Group
Poster #:
Day: 4/4/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Roosevelt 5, Marriott, Exhibition Level
Organizers: Dandong Yin, Yaping Cai, Shaowen Wang
Chairs: Yaping Cai

Call for Submissions

CyberGIS is defined as geographic information science and systems (GIS) based on advanced computing and cyberinfrastructure. Though geospatial big data have played important roles in many domains with significant societal impacts, geospatial data science remains to be established for advancing data-intensive geographic research and education in the era of big data and cyberGIS. At AAG 2019 annual meeting, the Symposium on Frontiers in Geospatial Data Science will be held to provide an exciting and timely forum for sharing recent progress and future trends on geospatial data science and related fields. A suite of paper and panel sessions will address cutting-edge advances of geospatial data science with a particular focus placed on the following themes: foundations, principles, and theories of geospatial data science; data-driven geography; artificial intelligence and data-intensive approaches to geographic problem solving; geographic knowledge discovery enabled by cyberGIS; education advances and challenges; and spatial cyberinfrastructure.


Geospatial data science represents an emerging interdisciplinary and transdisciplinary field intersecting among three broad knowledge domains: geospatial sciences and technologies, mathematical and statistical sciences, and cyberinfrastructure and computational sciences. The core of this intersection encompasses the synergies and interactions between big data and cyberGIS with geospatial principles guiding discovery and innovation.


Type Details Minutes Start Time
Presenter Jesse Bakker*, University of Minnesota, Toward a Scalable Agriculture Classification Model: Comparison of Remote Sensing Crop Classification Methods in the Red River Valley, Minnesota 20 1:10 PM
Presenter Jinpei Ou*, Sun Yat-sen University, Xiaoping Liu, Sun Yat-sen University, Estimating spatiotemporal variations of city-level energy-related CO2 emissions: an improved disaggregating model based on vegetation adjusted nighttime light data 20 1:30 PM
Presenter Yaping Cai*, University of Illinois, Shaowen Wang, University of Illinois, Kaiyu Guan, University of Illinois, Improving the Crop Type Classification Feasibility for Large Geographic Areas 20 1:50 PM
Presenter Rohit Mukherjee*, The Ohio State University, Desheng Liu, The Ohio State University, Comparison of statistical and deep learning methods for spatio-temporal fusion of satellite images 20 2:10 PM

To access contact information login