Häftad, Engelska, 2027
AI and Remote Sensing for Monitoring, Prediction, and Mitigation of Urban Climate Risks
Av Linlin Lu, Aqil Tariq, Qingting Li
2159 kr
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Beskrivning
The book begins by outlining the UHI crisis, highlighting the detrimental effects of urbanization on local climates, particularly the increased temperatures in urban environments. It emphasizes the role of remote sensing technologies and diverse data sources in analyzing UHIs, laying the groundwork for advanced research. The text then looks into AI-driven methodologies, showcasing how machine learning and deep learning techniques can enhance urban land classification, estimate urban 3D morphology, and retrieve land surface temperatures. Additionally, it discusses the application of deep learning for predicting UHIs and identifying the driving factors behind their formation, providing valuable insights into urban climate dynamics. In its final sections, the book addresses practical applications of these technologies, including the detection and assessment of heatwave events and urban heat health risk evaluations. It also emphasizes the importance of urban green infrastructure as a mitigation strategy and discusses the transition from data analysis to policy formulation. This essential resource illustrates how AI and remote sensing can be harnessed to monitor, predict, and mitigate urban climate risks effectively, fostering more resilient and sustainable cities.
- Examines AI-driven urban climate analysis methods with step-by-step workflows for applying machine learning (e.g., CNNs, transformers) to LST retrieval, fusion, and UHI prediction-with reproducible Python code and model weights
- Details policy-ready case studies with risk frameworks linking UHI patterns to actionable mitigation strategies (e.g., green infrastructure ROI, heat-health early warnings)
- Explains AI for UHI interpretability, including SHAP values, attention maps, and other AI tools to decode why models predict heat risks (e.g., "Park coverage reduces LST by 2�C vs. asphalt")
Produktinformation
- Utgivningsdatum: 2027-04-01
- Mått: 152 x 229 x undefined mm
- Format: Häftad
- Språk: Engelska
- Antal sidor: 350
- Förlag: Elsevier Science
- ISBN: 9780443452406
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Mer information
Dr. Linlin Lu is currently an Associate Professor at the Aerospace Information Research Institute, Chinese Academy of Sciences (AIR, CAS). Dr Lu obtained a PhD in remote sensing from the Institute of Remote Sensing Applications, CAS, in 2009. In the same year, she joined CAS as an assistant professor. Her research interests include image information detection, image classification, and time series analysis applied to urban environment, urban resilience and sustainability. She is the author of more than 140 journal articles and conference proceeding papers. Dr. Lu was appointed as a member of the Sino-EU Panel on Land and Soil (SEPLS) (2018-2022). She is a member of the Group on Earth Observations (GEO) Global Urban Observation and Information Initiative and Human Planet Initiative. She has served as a Scientific Committee member for the IEEE/GRSS International Geoscience and Remote Sensing Symposium (IGARSS) since 2016. She presently co-chairs the Urban Environment Working Group in the Digital Belt and Road program.Dr. Aqil Tariq is a Research Associate in the Department of Wildlife, Fisheries, and Aquaculture at Mississippi State University, USA. His research interests are 3D geoinformation, urban analytics, spatial analysis to examine land use/land cover, geospatial data science, urban planning, crop identification using SAR and optical satellite imagery, agriculture monitoring, forest fire, forest monitoring, forest cover dynamics, spatial statistics, multi-criteria algorithms, ecosystem sustainability, hazards risk reduction, statistical analysis and modeling (Google Earth Engine, HEC-RAS, FlowR, RAMMS, GeoClaw, COSI-Corr, SfM) using Python, R, and MATLAB. He is a member of different international science communities, i.e., Individual Membership of ISPRS, International Water Resources Association, International Association of Geodesy, and the Surveying & Spatial Sciences Institute (SSSI). He has attended more than 30 training workshops from the National Aeronautics and Space Agency (NASA) ARSET program.Dr. Qingting Li received the Ph.D. degree in remote sensing from the Institute of Remote Sensing Applications, Chinese Academy of Sciences (CAS), Beijing, China, in 2009. He is currently an Associate Professor with Aerospace Information Research Institute, CAS. His research interests include thermal infrared remote sensing, hyperspectral remote sensing, time-series analysis, and monitoring of natural resources and eco-environment with Earth observation data.
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