Mohammad Esmaeili | Wildfire Monitoring | Best Researcher Award

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Mr. Mohammad Esmaeili | Wildfire Monitoring | Best Researcher Award

Mr. Mohammad Esmaeili | Wildfire Monitoring – PHD Student at SBUK | Iran

Mr. M. Esmaeili is a dedicated researcher specializing in remote sensing, hyperspectral image processing, machine learning, and deep learning-based geospatial analytics. He is currently pursuing his Ph.D. in the Technical and Engineering Faculty of Shahid Bahonar University of Kerman, where his doctoral research focuses on developing advanced neural architectures for spatial–spectral feature extraction in Earth observation data with applications in environmental monitoring, wildfire detection, and precision agriculture. Throughout his academic journey, Mr. M. Esmaeili has been involved in collaborative international research projects with scholars from leading institutions across Iran, Europe, Central Asia, and the United States, contributing to major studies that utilize multi-sensor data fusion, morphological feature integration, and LiDAR-assisted crop classification frameworks. His professional experience includes research assistantship roles, participation in laboratory-based geospatial analysis teams, and contributions to multi-disciplinary research environments that integrate remote sensing, signal processing, and artificial intelligence. His research interests extend to spectral dimensionality reduction, spatial attention mechanisms, convolutional neural network model optimization, object-based image analysis, and wildfire burn area mapping from multispectral and hyperspectral satellite platforms. He possesses strong research skills in Python programming, deep learning model development, GIS software environments, spectral data preprocessing, quantitative accuracy evaluation, and scientific publishing. His academic contributions are reflected in multiple Scopus and IEEE indexed articles, which have gained citation recognition in the research community. Although early in his career, Mr. M. Esmaeili has received acknowledgment through research collaborations, peer-reviewed journal publications, conference participation, and contributions to emerging solutions in environmental remote sensing. His work demonstrates both scientific rigor and practical relevance in contemporary geospatial intelligence research. In conclusion, Mr. M. Esmaeili continues to strengthen his academic profile through high-impact research output, technical skill development, and sustained international research engagement, positioning him as a promising scholar capable of future leadership in remote sensing and intelligent Earth observation systems.

Academic Profile: Google Scholar

Featured Publications:

  1. Hyperspectral image band selection based on CNN embedded GA (CNNeGA). (2023). Citations: 104.

  2. ResMorCNN model: hyperspectral images classification using residual-injection morphological features and 3DCNN layers. (2023). Citations: 98.

  3. HypsLiDNet: 3-D–2-D CNN model and spatial–spectral morphological attention for crop classification with DESIS and LiDAR data. (2024). Citations: 73.

  4. DESSA-net model: Hyperspectral image classification using an entropy filter with spatial and spectral attention modules on DeepNet. (2024). Citations: 17.