The Daily Bulletin Archive

Qimin Cheng, visiting researcher in Buffalo State's Geography and Planning Department, and Zhenfeng Shao, visiting researcher in the University at Buffalo's Department of Urban and Regional Planning, will present "Representation, Retrieval, and Interpretation of Big Data in Remote Sensing and Applications" on Thursday, May 15, from 12:15 to 1:20 p.m. in Classroom Building A207. An abstract of their talk appears below.

Cheng is an associate professor at the Huazhong University of Science and Technology in China. Her research interests include remote sensing image processing and analysis, multimedia information extraction, and content-based image retrieval of high-resolution remote sensing images.

Shao is a professor in the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) at Wuhan University in China. His research interests are image retrieval, image fusion, 3-D reconstruction, and urban remote sensing application.

This event is supported by the Geography and Planning Department, International and Exchange Programs, the Data Science Initiative Group, and the School of Natural and Social Sciences.

Abstract
Big data has been becoming the focus of scientific research and been developing into one of the most important strategic resources of business and economic development in modern society. The term was originally defined in computer science. Among different manifestations that “big data” provides, it has three specific characteristics of “big”: volume, velocity, and variety. Obviously, big data not only brings wonderful opportunities but also creates great challenges for applications in our human society for managing, processing, mining, and further utilizing digital information or data.

Remotely sensed data using a variety of equipment, platforms, and sensors is typically big data simply because it carries the three V characteristics. In addition, big data processing in remote sensing (RS) encounters some unique challenges that other data sources do not. Data-driven techniques demonstrate strong potentials in data processing. Based on these points, this report applies the method of data-driven services with multiple data sources from RS studies to demonstrate the problem solving in big data. The main topics are as follows:

• Understanding research problems of RS big data
• Breaking through the bottlenecks in RS big data
• Some applications of remote sensing big data, which include image retrieval and detection, LiDAR applications for geographic feature extraction and 3-D reconstruction, optical remote sensing in urban design, emergency rescue planning, environmental monitoring, and hazard management.