Dr. Shengli Wang | Remote Sensing Image Processing | Best Academic Researcher Award
China University of Mining and Technology, China
Dr. Shengli Wang is a researcher and academic affiliated with the China University of Mining and Technology. His expertise spans deep learning, remote sensing, image processing, and change detection, with a focus on integrating artificial intelligence into geospatial analysis. Over the years, Dr. Wang has contributed significantly to advancements in interpreting and processing high-resolution satellite imagery. He has published in several high-impact journals and preprint platforms, positioning himself as a prominent figure in the application of machine learning to earth observation. His work is characterized by a practical and innovative approach to addressing the challenges of dynamic land cover changes, urban development monitoring, and spatial data extraction.
Profile
Education
Dr. Wang has pursued his education in China, earning degrees that prepared him for a career at the intersection of geoinformatics and computer science. His academic path has been rooted in the rigorous study of both theoretical and applied remote sensing, providing him with a strong foundation in both traditional image interpretation and cutting-edge computational techniques. This multidisciplinary background allowed him to effectively blend the fields of environmental science, engineering, and artificial intelligence.
Experience
Currently serving at the China University of Mining and Technology, Dr. Wang has been actively involved in both teaching and research. His role extends beyond academic responsibilities to include supervision of student research projects and collaborative efforts with fellow scientists in related disciplines. He has also participated in various scientific conferences and peer-review activities, reinforcing his status as an engaged contributor to the global research community. His involvement in interdisciplinary projects has enabled him to lead initiatives that bring practical utility to theoretical advancements in AI and remote sensing.
Research Interest
Dr. Wang’s primary research interests revolve around deep learning for change detection, high-resolution remote sensing image analysis, and automated extraction of spatial features. He is particularly focused on the development of neural network architectures such as ConvTransformers for improving the precision of object detection and change monitoring in satellite images. His work often involves the fusion of vector data with raster imagery, allowing for more accurate modeling of building shapes, land use dynamics, and environmental shifts. His methods often integrate edge detection, principal component analysis, and attention-based learning systems to enhance detection reliability.
Award
While the documents do not list specific awards, Dr. Wang’s continued presence in high-ranking journals and reputable conferences implies a recognition of his research quality and its relevance to both academic and industrial applications. His ability to consistently publish in respected outlets like IEEE and ISPRS indicates peer acknowledgment of his contributions to the field.
Publication
Dr. Wang has authored and co-authored several influential papers, often cited by other researchers for their methodological innovations and applied relevance. His representative publications include:
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“Change Detection Based on Existing Vector Polygons and Up-to-Date Images Using an Attention-Based Multi-Scale ConvTransformer Network”, published in Remote Sensing, May 2024 – Cited for its novel hybrid approach to polygon- and pixel-based change detection.
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“A Building Shape Vectorization Hierarchy From VHR Remote Sensing Imagery Combined DCNNs-Based Edge Detection and PCA-Based Corner Extraction”, published in IEEE J-STARS, December 2022 – Recognized for its application of deep convolutional neural networks to urban structure modeling.
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“Fine Object Change Detection Based on Vector Boundary and Deep Learning With High-Resolution Remote Sensing Images”, published in IEEE J-STARS, April 2022 – Widely cited for enhancing boundary-level accuracy in object detection.
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“Adaptive Surface Modeling of Soil Properties in Complex Landforms”, published in ISPRS Int. J. Geo-Inf., June 2017 – Referenced in terrain modeling and digital soil mapping studies.
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“A Grid-Based Hierarchical Representation Method for Large-Scale Scene Based on 3DGS”, Preprint, April 2025 – Gaining attention for its potential in 3D scene reconstruction.
Each of these contributions has been referenced in related research, reflecting their impact on the advancement of remote sensing methodologies and practical applications in land monitoring and smart city planning.
Conclusion
Dr. Shengli Wang exemplifies the modern interdisciplinary researcher who bridges technical innovation with real-world applications. His contributions to the field of remote sensing, particularly in the area of change detection using deep learning, continue to support the advancement of geospatial sciences. With a steady output of peer-reviewed publications and ongoing engagement in academic discourse, Dr. Wang is poised to remain an influential figure in the growing integration of artificial intelligence with earth observation technologies.