Mohammad Marjani | Environmental Science | Best Researcher Award

Memorial University of Newfoundland | Canada

Dr. Mohammad Marjani is a dedicated researcher and academic specializing in remote sensing, geospatial intelligence, and artificial intelligence applications for environmental monitoring. He is currently pursuing a Ph.D. in Electrical and Computer Engineering at Memorial University of Newfoundland, where his research focuses on developing advanced remote sensing and deep learning algorithms for environmental and climate-related analysis under the supervision of Dr. Masoud Mahdianpari. He earned his Master of Science in Geospatial Information Systems from K.N. Toosi University of Technology, where he conducted innovative research on wildfire spread modeling using deep learning techniques. His undergraduate degree in Geodesy and Geomatic Engineering from the same university explored 3D change detection methods in point clouds. His academic journey reflects a strong interdisciplinary foundation in remote sensing, GIS, machine learning, and computer vision, particularly applied to natural disaster management and environmental systems. Dr. Marjani has contributed as a peer reviewer for high-impact journals such as IEEE Geoscience and Remote Sensing Letters, Theoretical and Applied Climatology, and Remote Sensing. Professionally, he serves as a Research Scientist at C-CORE, where he develops AI-driven environmental modeling algorithms using satellite data. Alongside his research, he has demonstrated academic leadership through multiple teaching assistantships, delivering courses in C++, MATLAB, and Python programming across topics such as computational intelligence and image processing. He is also a co-founder of GeoHoosh, an educational group dedicated to promoting artificial intelligence applications in geomatics and geospatial engineering. Dr. Marjani’s research interests encompass wildfire modeling, satellite image analysis, WebGIS, and GeoAI, reflecting his commitment to advancing the integration of artificial intelligence with geospatial sciences for sustainable environmental solutions.

Profile: Scoups | Orcid | Google Scholar

Featured Publications

• Marjani, M., Ahmadi, S. A., & Mahdianpari, M. (2023). FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction. Ecological Informatics, 78, 102282.

• Marjani, M., Mahdianpari, M., & Mohammadimanesh, F. (2024). CNN-BiLSTM: A Novel Deep Learning Model for Near­-Real-Time Daily Wildfire Spread Prediction. Remote Sensing, 16(8), 1467.

• Marjani, M., & Mesgari, M. S. (2023). The large-scale wildfire spread prediction using a multi-kernel convolutional neural network. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 10(4/W1-2022), pp. 483-488.

• Marjani, M., Mohammadimanesh, F., Varon, D. J., Radman, A., & Mahdianpari, M. (2024). PRISMethaNet: A novel deep learning model for landfill methane detection using PRISMA satellite data. ISPRS Journal of Photogrammetry and Remote Sensing, 218, 802-818.

• Bahrami, H., McNairn, H., Mahdianpari, M., & Homayouni, S. (2022). A meta-analysis of remote sensing technologies and methodologies for crop characterization. Remote Sensing, 14(22), 5633.