Liping Yang

- Faculty -

Assistant Professor

Photo: Liping Yang
Geography & Environmental Studies; Computer Science
Ph.D. Spatial Informatics, University of Maine
Curriculum vitae


An assistant professor of geographic information science (GIScience) and geospatial artificial intelligence (GeoAI) at the University of New Mexico. Liping directs the Geospatial Artificial Intelligence Research and Visualization (GeoAIR) Laboratory. Liping was a postdoctoral research associate in the Information Sciences group at Los Alamos National Laboratory (LANL), focusing on computer vision and machine learning algorithm development for technical diagram image analysis. Before joining LANL, Liping was a postdoc researcher on big geospatial data mining and geovisualization in the GeoVISTA Center at Penn State University. Liping has worked many years at the intersection of GIScience, Computer Science, and Mathematics. Her multidisciplinary background on GIScience, graph theory, computational geometry, and machine learning provides her a solid foundation to develop creative and novel solutions to advance machine vision.

Recent Publications:

  • Yang, L., & Worboys, M. (2015). Generation of navigation graphs for indoor space. International Journal of Geographical Information Science29(10), 1737-1756.
  • Yang, L., & Cervone, G. (2019). Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event. Soft Computing23(24), 13393-13408.
  • Yang, L., MacEachren, A. M., Mitra, P., & Onorati, T. (2018). Visually-enabled active deep learning for (geo) text and image classification: a review. ISPRS International Journal of Geo-Information7(2), 65.
  • Pan, Y., Zhang, X., Cervone, G., & Yang, L. (2018). Detection of asphalt pavement potholes and cracks based on the unmanned aerial vehicle multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing11(10), 3701-3712.
  • Yang, L., Oyen, D., & Wholberg, B. (2019). A novel algorithm for skeleton extraction from images using topological graph analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops.