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.
I will lead the “Geospatial Data Analytics” group (prospective) at the Ohio State University (starting from February 2016 onwards) in the Department of Civil, Environmental and Geodetic Engineering (CEGE) to address the problems related to geospatial data acquisition and analytics. Our research will be under the general background of Remote Sensing, Photogrammetry and Computer Vision, with focuses spanning from the fundamental geospatial data processing and remote sensing analytics towards relevant civil, environmental applications. In particular, we will be interested in:
· Mathematical modeling of the sensory geometry and multiple sensory data alignment.
· Efficient Large-scale dense image matching using satellite images and frame cameras.
· Environmental monitoring and disaster responses.
· Information retrieval using multi-dimensional data (spatial, spectral and depth), 2D/3D scene classification 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 bringing the gaps for interdisciplinary researches.
Opportunities
Seeking for graduate students in 2016 fall.
Graduate research associate (RA) and post-doctoral researcher support may be available on a project-basis. Graduate teaching associate (TA) and fellowships may be offered by the department and university. Visiting scholars are welcome to contact me for collaborations.
I am interested in motivated students with solid basics in mathematics/statistics and computer programming (e.g. C/C++), preferably having a background in remote sensing or 3D computer vision/photogrammetry. If you are interested in our Ph.D. program and would like to work in our group, please feel free to contact me.
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.
I will lead the “Geospatial Data Analytics” group (prospective) at the Ohio State University (starting from February 2016 onwards) in the Department of Civil, Environmental and Geodetic Engineering (CEGE) to address the problems related to geospatial data acquisition and analytics. Our research will be under the general background of Remote Sensing, Photogrammetry and Computer Vision, with focuses spanning from the fundamental geospatial data processing and remote sensing analytics towards relevant civil, environmental applications. In particular, we will be interested in:
· Mathematical modeling of the sensory geometry and multiple sensory data alignment.
· Efficient Large-scale dense image matching using satellite images and frame cameras.
· Environmental monitoring and disaster responses.
· Information retrieval using multi-dimensional data (spatial, spectral and depth), 2D/3D scene classification 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 bringing the gaps for interdisciplinary researches.
Opportunities
Seeking for graduate students in 2016 fall.
Graduate research associate (RA) and post-doctoral researcher support may be available on a project-basis. Graduate teaching associate (TA) and fellowships may be offered by the department and university. Visiting scholars are welcome to contact me for collaborations.
I am interested in motivated students with solid basics in mathematics/statistics and computer programming (e.g. C/C++), preferably having a background in remote sensing or 3D computer vision/photogrammetry. If you are interested in our Ph.D. program and would like to work in our group, please feel free to contact me.