Ziheng Sun, PhD

Research Focus

I am a practitioner of using the latest technologies such as machine learning and high- performance computing to seek answers to Earth scientific questions. He has invented RSSI, a novel index for recognizing artificial objects from high resolution aerial images, and proposed parameterless automatic classification solution for reducing the parameter-tuning burden on scientists. He has successfully used long short-term memory to learn the seasonal changes to better recognize crops from remote sensing images. He has a lot of experiences on building web-based geospatial information systems for better disseminating, processing, visualizing, and understanding spatial big data.

Current Projects

■ Collaborative Research: GI Catalytick Track: Cyberinfrastructure for Intelligent High-Resolution Snow Cover Inference from Cubesat Imagery, National Science Foundation

Select Publications

■ Z. Sun et al.,. Deep learning classification for crop types in North Dakota. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2020).

■ Z. Sun et al., Geoweaver: Advanced cyberinfrastructure for managing hybrid geoscientific AI workflows. ISPRS International Journal of Geo-Information 9(2), 119 (2020).

■ Z. Sun et al.,Advanced cyberinfrastructure for intercomparison and validation of climate models. Environmental Modelling & Software 123, 104559 (2020).

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