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鈥檚 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鈥檚
students, and postdocs and served as a member of dozens of doctoral dissertation committees.
Labs and Facilities
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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: .&苍产蝉辫;鈥�
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., 鈥婩ritschi, 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:.
鈥�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