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Vasit Sagan, Ph.D.

Professor of Geospatial Science and Professor of Computer Science; Director, Remote Sensing Lab

Deputy Director, Taylor Geospatial Institute; Associate Vice President for Geospatial Science, Office of the Vice President for Research and Partnership

Courses Taught

Introduction to GIS; Intermediate GIS; GIS in Civil Engineering; Introduction to Remote Sensing; Geospatial Methods in Environmental Studies; Microwave Remote Sensing: SAR principles, data processing and applications; InSAR - Synthetic Aperture Radar Interferometry; Applied Machine Learning

Education

Ph.D., Peking University, 2006

Research Interests

Research focus: Geospatial computer vision - an interdisciplinary field that involves remote sensing, photogrammetry, machine learning/AI, and imagery analysis for various applications. 

Sagan’s research focuses on developing state-of-the-art computer vision technologies, AI/machine learning, and sensor/information fusion algorithms for studying food and water security, ecosystems, and social instability from local to global scales. He has been PI/Co-PI on over $50M in grant funding and has authored over 150 peer-reviewed journal publications, many of which have been recognized through best paper awards. He has served on NASA review panels and reviewed several NSF proposals and numerous journal papers. He has also advised and mentored numerous doctoral students, master’s students, and postdocs and served as a member of dozens of doctoral dissertation committees.

Labs and Facilities



Publications and Media Placements

Select Media Placements


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Select Publications (out of 150)

Sarkar, S., Sagan, V., Bhadra, S., Pokharel, M., Fritchi, F. (2023). Soybean seed composition prediction from standing crops using PlanetScope satellite imagery and machine learning. ISPRS Journal of Photogrammetry and Remote Sensing, in press.

Nguyen, C.; Sagan, V., Skobalski J, Severo, J.I. (2023). Early detection of wheat yellow rust disease and its impact on terminal yield with multi-spectral UAV imagery. Remote Sensing, 15(13):3301. doi: 

Nguyen, C.; Sagan, V., Bhadra, S., Moose, S. (2023). UAV multisensory data fusion and multi-task deep learning for high-throughput maize phenotyping. Sensors, 23(4): 1827. doi: .


Sagan, V., Maimaitijiang, M., Sidike, P., Bhadra, S., Gosselin, N., Burnette, M., Demieville, J., Hartling, S., LeBauer, D., Newcomb, M., Pauli, D., Peterson, K.T., Shakoor, N., Sylianou, A., Zender, C., Mockler, T. (2022). Data-driven artificial intelligence for calibration of hyperspectral big data. IEEE Transactions on Geoscience and Remote Sensing, 60: 1-20,  Art no. 5510320. doi: .

Rhodes, K. & Sagan, V. (2022). Integrating remote sensing and machine learning for regional scale habitat mapping: advances and future challenges for desert locust monitoring. IEEE Geoscience and Remote Sensing Magazine, 10(1): 289-319. doi: .

Buffa, C., Sagan, V., Brunner, G., and Phillips, Z. (2022). Predicting terrorism in Europe with remote sensing, spatial statistics, and machine learning.  ISPRS Int. J. Geo-Inf., 11(4), 211. doi: .

Dilmurat, K., Sagan, V., Maimaitijiang, M., Moose, S., Fritschi, FB.. Estimating crop seed composition using machine learning from multisensory UAV data. Remote Sensing. 2022; 14(19):4786. doi:.

Sagan, V., Maimaitijiang, M., Bhadra, S., Maimaitiyiming, M., Brown, D.R., Sidike, P., Fritschi, F.  (2021). Field-scale crop yield prediction using multi-temporal WorldView-3 and PlanetScope satellite images and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 174: 265-281. doi: .

Adrian, J., Sagan, V., and Maimaitijiang, M. (2021). Sentinel SAR-optical fusion for crop type mapping using deep learning and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 175: 215-235. doi: .

Hartling, S., Sagan, V., and Maimaitijiang, M. (2021). Urban tree species classification using a UAV-based multi-sensor data fusion approach. GIScience & Remote Sensing, 58(8): 1250-1275. doi: .

Hartling, S., Sagan, V., and Maimaitijiang, M., Dannevik, W., and Pasken, R. (2021). Estimating tree-related power outages for regional utility network using airborne LiDAR data and spatial statistics. International Journal of Applied Earth Observation and Geoinformation, 100:102330. doi: .

Cota, G., Sagan, V., Maimaitijiang, M., and Freeman, K. (2021). Forest conservation with deep learning: A deeper understanding of human geography around the Betampona Nature Reserve, Madagascar.  Remote Sens., 13(17), 3495; doi: .

Sagan, V., Peterson, K.T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B.A., Maalouf, S., Adams, C. (2020). Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Science Reviews,  205: 103187. doi: .

Peterson, K.T., Sagan, V., John Sloan. (2020). Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing.  GIScience & Remote Sensing, 57(4): 510-525. doi: .

Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., Fritschi, F.  (2020). Unmanned Aerial System (UAS)-based crop yield prediction using multi-sensor data fusion and deep learning. Remote Sensing of Environment, 237:111537. doi: 

Maimaitiyiming, M., Sagan, V., Sidike, P., Maimaitijiang, M., Miller, A.J., Kwasniewski, M. (2020). Leveraging very high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal grapevine physiology. Remote Sens., 12(19), 3216. doi: . 
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Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N., Mockler, T.. (2020). Quantifying leaf chlorophyll concentration of sorghum from hyperspectral data using derivative calculus and machine learning. Remote Sensing, 12(13), 2082. doi: .

Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A., Erkbol, H., Fritschi, F. (2020). Crop monitoring using Satellite/UAV data fusion and machine learning. Remote Sensing, 12(9), 1357. doi: .&²Ô²ú²õ±è;​

Vilbig, J.M., Sagan, V., and Bodine, C. (2020). Archaeological surveying with LiDAR and photogrammetry: A comparative analysis at Cahokia Mounds. major revision. Journal of Archaeological Sciences: Reports (33): 102509. doi: .

Sagan, V., Maimaitijiang, M., Sidike, P., Eblimit, K., Peterson, K.T., Hartling, S., Esposito, F., Khanal, K., Newcomb, M., Pauli, D., Ward, R., Fritschi, F., Shakoor, N., Mockler, T. (2019). UAV-Based high resolution thermal imaging for vegetation monitoring, and plant phenotyping using ICI 8640 P, FLIR Vue Pro R 640 and thermoMap Cameras. Remote Sensing, 11(3), 330; doi: . . 

Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K.T., Peterson, J., Burken, J., â€‹Fritschi, F. (2019). UAV/Satellite multiscale data fusion for crop monitoring and early stress detection. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W13 (Best Paper Award).  

Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., Mockler, T., Fritschi, F. (2019). dPEN: deep Progressively Expanded Network for mapping of heterogeneous agricultural landscape using WorldView-3 imagery. Remote Sensing of Environment, 221: 756-772.

Maimaitijiang, M., Sagan, V., Sidike, P., Maimaitiyiming, M., Hartling, S., Peterson, K.T., Maw, M., Shakoor, N., Mockler, Todd, Fritschi, F. (2019). Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass estimation from Unmanned Aerial System-based RGB Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 151:27-41.

Gosselin, N., Sagan, V., Maimaitiyiming, M., Fishman, J., Belina, K., Podleski, A., Maimaitijiang, M., Bashir, A., Balakrishna, J., and Dixon, A. (2019).  Using visual ozone damage scores and spectroscopy to quantify soybean responses to background ozone. Remote Sensing, 12(1), 93; doi: .

Manley, P., Sagan, V., Fritschi, F.B., Burken, J.G. (2019). Remote sensing of explosives-induced stress in plants: Hyperspectral imaging analysis for remote detection of threats.  Remote Sensing, 11(15), 1827; doi: .


Hartling, H., Sagan, V., Sidike, P., Maimaitijiang, M., Carron, J. (2019). Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors, 19(6), 1284; doi: .

Sagan, V., Maimaitiyiming, M., Fishman, J. (2018). Effects of ambient ozone on soybean biophysical variables and mineral nutrient accumulation. Remote Sens., 10(4), 562; doi:.

​Sagan, V., Pasken, R., Zarauz, J., Krotkov, N. (2018). Monitoring SO2 trajectories in a complex terrain environment using CALIPUFF, OMI and MODIS data. International Journal of Applied Earth Observation and Geoinformation, 69: 99-109.

Peterson, K.T., Sagan, V., Sidike, P., Cox, A.L., Martinez, M. (2018). Suspended sediment concentration estimation from Landsat imagery along the lower Missouri and middle Mississippi Rivers using extreme learning machine. Remote Sens., 10(10), 1503; doi: 


Sidike, P., Asari, V., Sagan, V. (2018). Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: a new neural network paradigm for remote sensing image analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 146: 161-181.

Sidike, P., Sagan, V., Qumsiyeh, M., Maimaitijiang, M., Essa, A., and Asari, V. (2018). Adaptive Trigonometric Transformation Function with Image Contrast and Color Enhancement: Application to Unmanned Aerial System Imagery.  IEEE Geoscience and Remote Sensing Letters, 15(3): 404-408. 
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Loesch, E. & Sagan, V. (2018). SBAS Analysis of Induced Ground Surface Deformation from Wastewater Injection in East Central Oklahoma, USA.  Remote Sens., 10(2), 283; doi:.

Professional Organizations and Associations

  • 2020 - present, Member, National Geospatial Advisory Committee (NGAC), the U.S. Department of the Interior 
  • 2023 - present: Trustee, St. Louis Academy of Sciences 
  • 2018 - present: Associate Editor, ISPRS Journal of Photogrammetry and Remote Sensing
  • 2014-2015: President, Heartland Region, American Society for Photogrammetry and Remote Sensing (ASPRS)
  • 2012-2013: Vice President, Heartland Region, American Society for Photogrammetry and Remote Sensing (ASPRS)
  • 2008-Present: Member, American Society for Photogrammetry and Remote Sensing (ASPRS).
  • 2010-Present: Member, American Geophysical Union (AGU)
  • 2008-Present: Member, IEEE and IEEE Geoscience & Remote Sensing Society