Maitiniyazi Maimaitijiang

Title

Assistant Professor

Office Building

Wecota Hall

Office

115I

Mailing Address

Wecota Hall 115I
Geography & Geospatial Sciences-Box 0506
University Station
Brookings, SD 57007

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 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 (ASPRS)
• American Geophysical Union(AGU)
• Association of American Geographers (AAG)
• International Plant Phenotyping Network (IPPN)

Awards and Honors

• 2019, Best paper award of 2019 ISPRS international workshop on image and data fusion, Netherlands
• 2020, Saint Louis University Geospatial Research and Innovation Award
• 2020, Saint Louis University Excellence in Integrated and Applied Science Research Award
• 2021, Saint Louis University Distinguished Dissertation Award
• 2021, Remote Sensing Journal Best Paper Award
• 2022, Sensors Journal Outstanding Publication Award

Work Experience

• August 2021 - assistant professor, Department of Geography and Geospatial Sciences, Geospatial Sciences Center of Excellence,  South Dakota 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

Publications

Peer-Reviewed Publications:

1. Maimaitijiang M*., Millett, B., Paheding S., Khan SN., Dilmurat K., Reyes A., 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 SN., Maimaitijiang M*., Millett, B., Paheding S., Li DP., Caffé M., 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.; Fritschi, F.B. Estimating Crop Seed Composition Using Machine Learning from Multisensory UAV Data. 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 US 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., 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., 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., 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 multi-sensor data fusion approach. GIScience and Remote Sensing
12. Sagan, V., Maimaitijiang M., et al. (2021). Crop yield prediction using multi-temporal 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., Kwasniewski, MT. (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., 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., 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., 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., 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., 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., Zender, C. (2020), Data From: TERRA-REF, An open reference data set from high resolution genomics, phenomics and imaging sensors, v6, 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., 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, 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
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., 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., 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., 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., 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 multi-component 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 (UAS)-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., 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., 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., 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.