Geospatial Data Analytics

The emerging diversity of remote sensing platforms and sensor technologies offers many choices for geospatial data generation and processing. The increased accuracy, resolution and volume of these geospatial data raise more complex scenarios yet more challenges in data fusion, integration and analytics, leading to new aspects to applications such as large scale urban/environment monitoring, city modeling, scene understanding, indoor/outdoor mapping, navigation and precision agriculture, etc.

Dr.Rongjun Qin will lead the “Geospatial Data Analytics (GDA)” group at the Ohio State University in the Department of Civil, Environmental and Geodetic Engineering (CEGE) to address the problems related to geospatial data acquisition and analytics. Their research will be under the general background of Remote Sensing, Photogrammetry and Computer Vision, with focuses spanning from the fundamental geospatial data processing, remote sensing analytics and geospatial data management towards relevant civil, environmental applications. In particular, they will be interested in:

· Mathematical modeling of the sensory geometry and multiple sensory data alignment in various platforms (UAV, Satellite, Airborne data).
· Efficient Large-scale dense image matching using satellite images and frame cameras.
· Environmental monitoring and disaster responses.
· 3D city and landscape modeling, geographic information system.
· Information retrieval using multi-dimensional data (spatial, spectral and depth), 2D/3D scene classification, vision-based understanding and time-series data analysis.
· Identifying motivating applications in many fields that require accurate geometric and contextual geospatial information and developing efficient computational approaches for bridging the gaps for interdisciplinary research.