Maitiniyazi Maimaitijiang

Maitiniyazi Maimaitijiang

Assistant Professor

Biography

I am an assistant professor of remote sensing and geographic information systems with the Department of Geography and Geospatial Sciences at South à£à£Ö±²¥Ðã State University. I completed my Ph.D. program at Saint Louis University. My research interests fall in the general area of theory and applications of geospatial sciences and technologies, as well as computer vision and artificial intelligence/machine learning in the field of sustainable agriculture, food and water security from regional to global scales. Particularly, I focused on developing/implementing state-of-the-art geospatial tools and AI/machine learning methods in the field of precision agriculture and high-throughput plant phenotyping, plant biophysical and biochemical traits estimation and crop yield prediction, plant health and stress monitoring and disease detection using multimodal (multispectral, hyperspectral, RGB, thermal, LiDAR and SAR), multiscale (satellite, airborne/UAV and ground) and multitemporal remote sensing.

Education

  • Ph.D in environmental sciences and GIS | Saint Louis University | 2020
  • M.S. in geography | Sun Yat-sen University, Guangzhou, China
  • B.S. in natural resources and environmental sciences | Peking University, Beijing, China

Academic and Professional Experience

Academic Interests
  • Remote sensing in precision agriculture, plant phenotyping, water quality monitoring, land use/land cover changes, urban growth and urban microclimate, SAR/InSAR and land subsidence.
  • Geospatial analytics, image processing and photogrammetry.
  • AI/machine learning in geospatial and remote sensing-related studies.
Academic Responsibilities

Current courses teaching:

  • GEOG 483/583: Aerial/UAS Remote Sensing (spring)
  • GEOG 750: Agricultural Remote Sensing (fall)
  • GEOG 784: Machine Learning for Remote Sensing (spring)
  • GEOG 790: Deep Learning for Remote Sensing (fall)
Committees and Professional Memberships
  • Guest editor of the special issue "Deep Learning for Remote Sensing Image Classification" of Remote Sensing Journal.
  • Guest editor of the special Issue "Digital Agricultural Production Based on Remote Sensing Technology, AI Applications and Robotic Systems" of Remote Sensing Journal.
  • American Society for Photogrammetry and Remote Sensing
  • American Geophysical Union
  • Association of American Geographers
  • International Plant Phenotyping Network
Work Experience
  • August 2021-present, assistant professor, Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence, South à£à£Ö±²¥Ðã State University
  • March 2021-August 2021, remote sensing and imagery scientist, Geospatial Institute, Saint Louis University
  • June 2020-March 2021, postdoctoral research fellow, Remote Sensing Lab, Saint Louis University
  • August 2016-June 2020, research assistant, Saint Louis University

Research and Scholarly Work

à£à£Ö±²¥Ðãs and Honors
  • 2019, Best Paper à£à£Ö±²¥Ðã of 2019 International Society for Photogrammetry and Remote Sensing international workshop on image and data fusion, Netherlands
  • 2020, Saint Louis University Geospatial Research and Innovation à£à£Ö±²¥Ðã
  • 2020, Saint Louis University Excellence in Integrated and Applied Science Research à£à£Ö±²¥Ðã
  • 2021, Saint Louis University Distinguished Dissertation à£à£Ö±²¥Ðã
  • 2021, Remote Sensing Journal
  • 2022, Sensors Journal
Publications

Peer-Reviewed Publications:

  1. Maimaitijiang, M*., Millett, B., Paheding, S., Khan, S.N., Dilmurat, K., Reyes, A. and Kovács, P. (2023). Estimating crop grain yield and seed composition using deep learning from UAV multispectral data. 2023 IEEE International Geoscience and Remote Sensing Symposium IGARSS (accepted).
  2. Khan, S.N., Maimaitijiang, M*., Millett, B., Paheding, S., Li, D.P., Caffé, M. and Kovács, P. (2023). Simultaneously estimating crop yield and seed composition using multitask learning from UAV multispectral data. 2023 IEEE International Geoscience and Remote Sensing Symposium IGARSS (accepted).
  3. Luo, D., Zhang, H.K., Houborg, R., Ndekelu, L.M., Maimaitijiang, M., Tran, K.H. and McMaine, J. (2023). Utility of daily 3m Planet Fusion Surface Reflectance data for tillage practice mapping with deep learning. Science of Remote Sensing, 100085.
  4. Dilmurat, K., Sagan, V., Maimaitijiang, M., Moose, S. and Fritschi, F.B. . Remote Sensing. 2022, 14, 4786. DOI:10.3390/rs14194786.
  5. Khan, S.N., Li, D. and Maimaitijiang, M. (2022). A geographically weighted random forest approach to predict corn yield in the U.S. Corn Belt. Remote Sensing, 14(12), 2843.
  6. Sharma, P., Leigh, L., Chang, J., Maimaitijiang, M. and Caffé, M. (2022). Above-ground biomass estimation in oats using UAV remote sensing and machine learning. Sensors, 22(2), 601.
  7. 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. and Mockler, T. (2021). Data-driven artificial intelligence for calibration of hyperspectral big data. IEEE Transactions on Geoscience and Remote Sensing, 1-20.
  8. Maimaitijiang, M., Sagan, V. and Fritschi, F. B. (2021, July). Crop yield prediction using satellite/UAV synergy and machine learning. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6276-6279). IEEE.
  9. Maimaitijiang, M., Sagan, V., Bhadra, S., Nguyen, C., Mockler, T. and Shakoor, N. (2021). A fully automated and fast approach for canopy cover estimation using high-resolution remote sensing imagery. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2021, 219-226.
  10. 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.
  11. Hartling, S., Sagan, V. and Maimaitijiang, M. (2021). Urban tree species classification using a UAV-based multisensor data fusion approach. GIScience and Remote Sensing.
  12. Sagan, V., Maimaitijiang M., et al. (2021). Crop yield prediction using multitemporal Worldview-3 and Planet satellite images and deep learning. ISPRS Journal of Photogrammetry and Remote Sensing, 174: 265-281.
  13. Hartling, S., Sagan, V., Maimaitijiang, M., et al.  (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.
  14. 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 Sensing. 13(17), 3495.
  15. Nguyen, C., Sagan, V., Maimaitiyiming, M., Maimaitijiang, M., Bhadra, S. and Kwasniewski, M.T. (2021). Early detection of plant viral disease using hyperspectral imaging and deep learning. Sensors, 21(3): 742.
  16. Maimaitijiang, M., Sagan, V., Erkbol, H., Adrian, J., Newcomb, M., LeBauer, D., Pauli, D., Shakoor, N. and Mockler, T. (2020). UAV-based sorghum growth monitoring: a comparative analysis of LiDAR and photogrammetry. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2020, 489-496.
  17. Maimaitijiang, M., Sagan, V., Sidike, P., Daloye, A., Erkbol, H. and Fritschi, F. (2020). Crop monitoring using satellite/UAV data fusion and machine learning. Remote Sensing, 12(9), 1357.
  18. Maimaitijiang M., Sagan, V., Sidike, P., Hartling, S., Esposito, F. and Fritschi, F. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237:111537.
  19. Sagan, V., Peterson, K.T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B.A., Maalouf, S. and 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, 103187.
  20. Bhadra, S., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Newcomb, M., Shakoor, N. and Mockler, T. (2020). Quantifying leaf chlorophyll concentration of sorghum from hyperspectral data using derivative calculus and machine learning. Remote Sensing, 12(13), 2082.
  21. LeBauer, D., Maxwell, B., Demieville, J., Fahlgren, N., French, A., Garnett, R., Hu, Z., Huynh, K., Kooper, R., Li, Z., Maimaitijiang, M., Mao, J., Mockler, T., Morris, G., Newcomb, M., Ottman, M., Ozersky, P., Paheding, S., Pauli, D., Pless, R., Qin, W., Riemer, K., Rohde, G., Rooney, W., Sagan, V., Shakoor, N., Stylianou, A., Thorp, K., Ward, R., White, J., Willis, C. and Zender, C. (2020), , Vol. 6, Dryad, Dataset, DOI:10.5061/dryad.4b8gtht99.
  22. Maimaitiyiming, M., Maimaitijiang M., et al. (2020). Modeling early indicators of grapevine physiology using hyperspectral imaging and partial least squares regression (PLSR), IEEE IGRASS-2020.
  23. Maimaitiyiming, M., Sagan, V., Sidike, P., Maimaitijiang, M., Miller, A.J. and Kwasniewski, M. (2020). Leveraging very high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal grapevine physiology. Remote Sensing, 12(19), 3216.
  24. Maimaitijiang M., Sagan, V., Sidike, P., Maimaitiyiming, M., Hartling, S., Peterson, K.T., Maw, M., Shakoor, N., Mockler, Todd and Fritschi, F. (2019). Vegetation Index Weighted Canopy Volume Model for soybean biomass estimation from unmanned aerial system-based RGB Imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 151: 27-41.
  25. 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., and 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.
  26. Sagan, V., Maimaitijiang M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., Peterson, K.T., Peterson, J., Burken, J. and 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.
  27. Sidike, P., Sagan, V., Maimaitijiang M., Maimaitiyiming, M., Shakoor, N., Burken, J., Mockler, T., and 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.
  28. Muhammad, W., Esposito, F., Maimaitijiang M., Sagan, V. and Bonaiuti E. (2019). Polly: A tool for rapid data integration and analysis in support of agricultural research and education. Internet of Things. 100141.
  29. Babaeian, E., Sidike, P., Newcomb, M. S., Maimaitijiang M., White, S. A., Demieville, J. and Sagan, V. (2019). A new optical remote sensing technique for high-resolution mapping of soil moisture. Frontiers in Big Data, 2, 37.
  30. 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.
  31. Numbere, A. O. and Maimaitijiang M. (2019). Mapping of nypa palm invasion of mangrove forest using low cost and high resolution UAV digital imagery in the Niger delta Nigeria. Current Trends in Forest Research (ISSN: 2638-0013).
  32. Hartling, S., Sagan, V., Sidike, P., Maimaitijiang M. and Carron, J. (2019). Urban tree species classification using a Worldview-2/3 and LiDAR data fusion approach and deep learning. Sensors, 19(6), 1284.
  33. 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.
  34. Sidike, P., Sagan, V., Asari, V. and Maimaitijiang M. (2018). A multicomponent-based volumetric directional pattern for texture feature extraction from hyperspectral imagery. In Pattern Recognition and Tracking XXIX (Vol. 10649, p. 1064910). International Society for Optics and Photonics.
  35. Maimaitijiang M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K. and Burken, J. (2017). Unmanned Aerial System-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43-58.
  36. Maimaitijiang M., Ghulam, A., Sandoval, J. O. and Maimaitiyiming, M. (2015). Drivers of land cover and land use changes in St. Louis metropolitan area over the past 40 years characterized by remote sensing and census population data. International Journal of Applied Earth Observation and Geoinformation, 35, 161-174.
  37. Ghulam, A., Ghulam, O., Maimaitijiang M., Freeman, K., Porton, I. and Maimaitiyiming, M. (2015). Remote sensing based spatial statistics to document tropical rainforest transition pathways. Remote Sensing, 7(5), 6257-6279.
  38. Ghulam, A., Fishman, J., Maimaitiyiming, M., Wilkins, J. L., Maimaitijiang M., Welsh, J. and Grzovic, M. (2015). Characterizing crop responses to background ozone in open-air agricultural field by using reflectance spectroscopy. IEEE Geoscience and Remote Sensing Letters, 12(6), 1307-1311.
  39. Maimaitijiang M. and Alimujiang, K. (2018). Spatial-temporal change of Urumqi urban land use and land cover based on grid cell approach. Transactions of the Chinese Society of Agricultural Engineering, 34(1), 210-216.
  40. Ziji E., Alimujiang K. and Maimaitijiang M. (2018). Temporal and spatial variations of urban land cover/land use based on grid element in northwest arid city of China. Arid Land Geography, 41(3): 625-633.
  41. Maimaitijiang, M. and Alimujiang, K. (2015). Study on land surface characteristics and its relationship with land surface thermal environment of typical city in arid region. Ecology and Environmental Sciences, 24(11), 1865-1871.
Mailing Address:
Wecota Hall 115I
Geography & Geospatial Sciences-Box 0506
University Station
Brookings, SD 57007
Office Location:
Wecota Hall
Room 115I
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