Dr. Weiwei Song is a Lecturer at the Faculty of Land Resources Engineering, Kunming University of Science and Technology. Born in 1976, he specializes in spatiotemporal cloud computing, large-scale spatial data analytics, spatiotemporal artificial intelligence, and ecological-environmental monitoring. His research has significantly contributed to geospatial technology and natural resource management, with multiple ongoing projects funded by national and provincial agencies.
Publication Profile
Scopus
Educational Background 🎓
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Ph.D. in Earth Exploration and Information Technology – Kunming University of Science and Technology (2015)
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Postdoctoral Research – George Mason University, USA (2017)
Professional Experience 💼
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Lecturer (2018–Present) – Faculty of Land Resources Engineering, Kunming University of Science and Technology
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Led multiple research projects in geospatial technology and artificial intelligence applications for environmental monitoring and natural resource management
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Guest Editor for Annals of GIS, Special Issue: “Advancements of GIS in the New IT Era”
Research Interests 🔬
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Spatiotemporal Cloud Computing
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Big Spatiotemporal Data Analytics
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Artificial Intelligence in Geospatial Analysis
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Ecological-Environmental Monitoring
Awards and Honors🏆✨
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2019 – Second Prize, Science & Technology Progress Award (Chinese Society for Surveying, Mapping and Geoinformation)
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2020 – Engineering Gold Medal (China Association for Geographic Information Industry)
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2021 – First Prize, Yunnan Provincial Geomatics Advancement Award
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2022 – Third Prize, Yunnan Science & Technology Progress Award
Conclusion🌟
Dr. Weiwei Song is an accomplished researcher in geospatial sciences, with expertise in cloud computing and artificial intelligence applications for environmental monitoring. His contributions to major government-funded projects and recognition in scientific communities highlight his impact in the field. Through his academic and research endeavors, he continues to advance spatiotemporal data analytics and geospatial technologies, enhancing decision-making processes in natural resource management.
Publications 📚
1️⃣ Wei, Q., et al. (2025). 🛰️ Spatiotemporal estimation of surface NO₂ concentrations in the Pearl River Delta region based on TROPOMI data and machine learning. Atmospheric Pollution Research, 16(3): 102353.
2️⃣ Li, J., et al. (2024). 🌍 Study on Spatio-Temporal Indexing Model of Geohazard Monitoring Data Based on DataStream Clustering Algorithm. ISPRS International Journal of Geo-Information, 13(3): 93.
3️⃣ Chen, J., et al. (2024). 🤖 1DCAE-TSSAMC: Two-Stage Multi-Dimensional Spatial Features Based Multi-View Deep Clustering for Time Series Data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 32(04): 593-623.
4️⃣ You, N., et al. (2023). 🖼️ Research on image denoising in edge detection based on wavelet transform. Applied Sciences, 13(3): 1837.
5️⃣ You, N., et al. (2023). 🏞️ Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection. Applied Sciences, 13(7): 4454.
6️⃣ Lu, J., et al. (2023). 🍏 A Study of Apple Orchards Extraction in the Zhaotong Region Based on Sentinel Images and Improved Spectral Angle Features. Applied Sciences, 13(20): 11194.
7️⃣ Li, Y., et al. (2023). ☁️ A SqueeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment. Applied Sciences, 13(3): 1869.
8️⃣ Song, W. and C. Wu (2021). 🗺️ Introduction to advancements of GIS in the new IT era. Taylor & Francis, 27: 1-4.
9️⃣ Hu, F., et al. (2020). 💾 A hierarchical indexing strategy for optimizing Apache Spark with HDFS to efficiently query big geospatial raster data. International Journal of Digital Earth, 13(3): 410-428.
🔟 Jin, B., et al. (2019). 🛰️ Object-oriented method combined with deep convolutional neural networks for land-use-type classification of remote sensing images. Journal of the Indian Society of Remote Sensing, 47: 951-965.