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Vasit Sagan, Ph.D.

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

Director, Saint Louis University Geospatial Center

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.

Vasit Sagan, Ph.D., is professor of geospatial science and computer science, director of the Saint Louis University Geospatial Center, and SLU site director for the NSF-IUCRC Center for Accurate Georeferencing of the Environment. He previously served as the founding and acting director of the Taylor Geospatial Institute (TGI), where he led the consortium's launch and helped establish it as one of the nation's premier geospatial research institutes.

An internationally recognized scholar in remote sensing, artificial intelligence, computer vision, and digital agriculture, Sagan has secured more than $60 million in competitive research funding as principal investigator or co-principal investigator and has authored over 150 peer-reviewed publications. His pioneering research focuses on developing advanced geospatial and AI -driven solutions to address critical challenges in agriculture, natural resource management, and food and water security. His work has received numerous honors, including the Outstanding Scientist Award from the Academy of Science-St. Louis and multiple outstanding publication/research paper awards recognizing significant contributions to geospatial science and remote sensing.

Sagan has mentored 25 doctoral students, 35 master's students, and 16 postdoctoral researchers, while serving on numerous dissertation committees and advancing workforce development in geospatial science and artificial intelligence. Beyond academia, he plays a prominent leadership role in the national geospatial community. He serves on the U.S. Department of the Interior's National Geospatial Advisory Committee, previously chaired its Subcommittee on Geospatial Excellence and Innovation, and currently co-chairs the Landsat Advisory Group. Through these roles, he contributes to shaping national geospatial strategy, science priorities, and funding initiatives. He also serves as an associate editor of the ISPRS Journal of Photogrammetry and Remote Sensing, one of the world's leading journals in geospatial science.

Sagan is widely recognized for advancing the integration of geospatial technologies, artificial intelligence, and remote sensing to generate actionable insights that support scientific discovery, economic development and informed decision-making at local, national and global scales.

Labs and Facilities

Publications and Media Placements

Select Media Placements

Select Publications (out of 150)
  • Sagan, V., Rhodes, K., Bhadra, S., Alifu, H., Giri, A., Pawar, A., Roy, B., Sarkar, S., & Fritschi, F. (2026). Genotype-Aware Prediction of Soybean Seed Composition from Multimodal UAV Imagery of the Standing Crop. Remote Sensing, 18(18), 3121. https://doi.org/10.3390/rs18183121
  • Haireti, A., Sagan, V., Maiwulanjiang, A., Sarkar, S., Al Akkad, O., Roy, B., Ye, h., Raju, D., and Nguyen, H. (2026). Adversarial domain alignment improves root biomass prediction from SkySat imagery. IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2026.3693043 
  • Pawar, A., Sagan, V., Alifu, A., Tamang, K.R., Bagnall, G.C., Gul, C., Shrestha, N., Lopes, F., Topp, C., Valliyodan, B. (2026). A novel approach to estimate total carbon accounting for below and above-ground carbon content of industrial hemp with UAV LiDAR and hyperspectral data fusion. Smart Agriculture Technology, 13: 101897. doi: 10.1016/j.atech.2026.101897
  • Lopes, F. A., Sagan, V., Esposito, F., and Stylianou, A. (2025). Geospatial Time Machine: A generative model to enhance spectral-temporal data resolution. IEEE Trans. Geosci. Remote Sens., 63, 4407613, doi: 10.1109/TGRS.2025.3552629.
  • Roy, B., Sagan, V., Alifu, H., Saxton, J., Ghoreishi, D., Shakoor, N. (2025). Soil carbon estimation from hyperspectral imagery with wavelet decomposition and frame theory. IEEE Trans. Geosci. Remote Sens., 62, 4511312: 1-12. doi: 10.1109/TGRS.2024.3461628.
  • Said, I., Sagan, V., Peterson, K.T., Haireti, A., Maiwulanjiang, A., Stylianou, A., Al Akkad, O., Sarkar, S., Al Shakarji, N. (2025). Seed protein content estimation with bench-top hyperspectral imaging and attentive convolutional neural network models. Sensors, 25(2), 303; https://doi.org/10.3390/s25020303.
  • Sagan, V., Coral, R., Bhadra, S., Alifu, H. Al Akkad, O., Giri, A., Esposito, F. (2024). Hyperfidelis: A Software Toolkit to Empower Precision Agriculture with GeoAI. Remote Sens. 2024, 16(9), 1584; https://doi.org/10.3390/rs16091584
  • Skobalski, J., Sagan, V., Haireti, A., Akkad, O.A., Lopes, F., Grignola, F. (2024). Bridging the gap between crop breeding and GeoAI: Soybean yield prediction from multispectral UAV images with transfer learning. ISPRS J. Photogramm. Remote Sens., 210: 260-281. doi: 10.1016/j.isprsjprs.2024.03.015
  • Sarkar, S., Sagan, V., Bhadra, S., Fritschi, F. (2024). Spectral enhancement and expansion of PlanetScope images with Sentinal-2 data for improved soybean yield and seed composition estimation. Sci Rep 14, 15063. doi: 10.1038/s41598-024- 63650-3
  • Bhadra, S., Sagan, V., Sarkar, S., Braud, M., Mockler, T.C., Eveland, A. (2024). ROSAIL-Net: A transfer learning-based dual stream neural network to estimate leaf chlorophyll and leaf angle of crops from UAV hyperspectral images. ISPRS J. Photogramm. Remote Sens., 210: 1-24. doi:10.1016/j.isprsjprs.2024.02.020
  • Lopes, F. A., Sagan, V., & Esposito, F. (2024). PlantPlotGAN: A physics-informed generative adversarial network for plant disease prediction. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). doi:10.48550/arXiv.2310.18268
  • Giri, A., Sagan, V., Alifu, H., Maiwulanjiang, A., Sarkar, S., Roy, B., & Fritschi, F. B. (2024). A wavelet decomposition method for estimating soybean seed composition with hyperspectral data. Remote Sensing, 16(23): 4594. doi: 10.3390/rs16234594.
  • Giri, A., Sagan, V., Podgursky, M. (2024). The ballpark effect: spatial-data-driven insights into Baseball’s local economic impact. Applied Sciences. 2024; 14(18):8134. doi: 10.3390/app14188134
  • 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: 10.3390/rs15133301.
  • 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: 10.3390/s23041827.
  • 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 : 10.1109/TGRS.2021.3091409.
  • 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: 10.1109/MGRS.2021.3097280.
  • 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: 10.3390/ijgi11040211.
  • 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: 10.3390/rs14194786.
  • 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: 10.1016/j.isprsjprs.2021.02.008.
  • 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: 10.1016/j.isprsjprs.2021.02.018.
  • 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: 10.1080/15481603.2021.1974275.
  • 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: 10.1016/j.jag.2021.102330.
  • 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: 10.3390/rs13173495.
  • 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: 10.1016/j.earscirev.2020.103187.
  • 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: 0.1080/15481603.2020.1738061.
  • 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: 10.1016/j.rse.2019.111599.
  • 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: 10.3390/rs12193216.
  • 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: 10.3390/rs12132082.
  • 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: 10.3390/rs12091357.
  • 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: 10.1016/j.jasrep.2020.102509.
  • 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: 10.3390/rs11030330. 2021 Best Paper Award.
  • Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K.T., Peterson, J., Burken, J., 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 ). PDF
  • 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: 10.3390/rs12010093.
  • 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: 10.3390/rs11151827.
  • 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: 10.3390/s19061284.
  • 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: 10.3390/rs10040562.
  • 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: 10.3390/rs10101503
  • 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.
  • 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: 10.3390/rs10020283.

Professional Organizations and Associations

  • 2020-2026. Member, National Geospatial Advisory Committee (NGAC), the U.S. Department of the Interior
  • 2023 to present: Trustee and Executive Board Member, St. Louis Academy of Sciences
  • 2018 to 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 to present: Member, American Society for Photogrammetry and Remote Sensing (ASPRS).
  • 2010 to present: Member, American Geophysical Union (AGU)
  • 2008 to present: Member, IEEE and IEEE Geoscience & Remote Sensing Society