A platform for research: civil engineering, architecture and urbanism
Handbook of geospatial approaches to sustainable cities
Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- PART I - Artificial Intelligence and Big Data Analytics: Needs and Requirements -- PART II - Geospatial Techniques for Renewable Cities -- PART III - Geospatial Techniques for Resilient Cities -- PART IV - Digital Cities -- Acknowledgments -- Editors -- Contributors -- Part I: Artificial Intelligence and Big Data Analytics: Needs and Requirements -- 1. Sensing Urban Physical Environment with GeoAI and Street-Level Imagery -- Introduction -- Street-Level Imagery (SI) -- Street-Level Imagery Sources -- Street-Level Imagery Attributes -- Methods for Sensing the Urban Physical Environment -- Sensing Tasks -- Scene Element Extraction -- Scene Perception -- Scene Inference -- Scene Embedding -- Scene Generation -- Approaches Based on Techniques -- Supervised Learning -- Unsupervised Learning -- Semi-Supervised Learning -- Reinforcement Learning -- Applications of Urban Sensing -- Observational Sensing: Sensing the Physical Entity -- Instance-Level Sensing -- Street-Level Sensing -- Neighborhood-Level Sensing -- City-Level Sensing -- Urban Implicit Relationship Sensing: Sensing Beyond the Image -- Human Perception Sensing: Sensing with Human-Centric Perspective -- Future Trends -- References -- 2. Geospatial Big Data for Urban Sustainability Science -- Urban Sustainability Science -- Geospatial Big Data -- Application of Using Geospatial Big Data -- Agglomeration Economies -- Urban Freight -- Urban Functions and Transit-Oriented Development (TOD) -- Conclusions -- Future Challenges -- References -- 3. Urban Flooding Monitoring and Management in Geospatial Perspective: Data, Techniques, and Platforms -- Urban Sustainability Science -- Geospatial Data for Urban Flooding Monitoring -- Geospatial Techniques for Urban Flooding Management.
"This comprehensive handbook presents the current state of knowledge on geospatial technologies, techniques, and methods that are imperative for providing solutions to sustainable cities. It addresses the role of geospatial big data and AI techniques and how they are applied when analyzing the sustainability of urban development, land use, urban planning, and resource management, as well as monitoring the impact urbanization has on the environment and the ecosystem. With contributions from renowned experts around the world, this holistic handbook is a toolbox for geospatial, urban, and sustainability professionals, and the artificial intelligence community"--
Handbook of geospatial approaches to sustainable cities
Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- PART I - Artificial Intelligence and Big Data Analytics: Needs and Requirements -- PART II - Geospatial Techniques for Renewable Cities -- PART III - Geospatial Techniques for Resilient Cities -- PART IV - Digital Cities -- Acknowledgments -- Editors -- Contributors -- Part I: Artificial Intelligence and Big Data Analytics: Needs and Requirements -- 1. Sensing Urban Physical Environment with GeoAI and Street-Level Imagery -- Introduction -- Street-Level Imagery (SI) -- Street-Level Imagery Sources -- Street-Level Imagery Attributes -- Methods for Sensing the Urban Physical Environment -- Sensing Tasks -- Scene Element Extraction -- Scene Perception -- Scene Inference -- Scene Embedding -- Scene Generation -- Approaches Based on Techniques -- Supervised Learning -- Unsupervised Learning -- Semi-Supervised Learning -- Reinforcement Learning -- Applications of Urban Sensing -- Observational Sensing: Sensing the Physical Entity -- Instance-Level Sensing -- Street-Level Sensing -- Neighborhood-Level Sensing -- City-Level Sensing -- Urban Implicit Relationship Sensing: Sensing Beyond the Image -- Human Perception Sensing: Sensing with Human-Centric Perspective -- Future Trends -- References -- 2. Geospatial Big Data for Urban Sustainability Science -- Urban Sustainability Science -- Geospatial Big Data -- Application of Using Geospatial Big Data -- Agglomeration Economies -- Urban Freight -- Urban Functions and Transit-Oriented Development (TOD) -- Conclusions -- Future Challenges -- References -- 3. Urban Flooding Monitoring and Management in Geospatial Perspective: Data, Techniques, and Platforms -- Urban Sustainability Science -- Geospatial Data for Urban Flooding Monitoring -- Geospatial Techniques for Urban Flooding Management.
"This comprehensive handbook presents the current state of knowledge on geospatial technologies, techniques, and methods that are imperative for providing solutions to sustainable cities. It addresses the role of geospatial big data and AI techniques and how they are applied when analyzing the sustainability of urban development, land use, urban planning, and resource management, as well as monitoring the impact urbanization has on the environment and the ecosystem. With contributions from renowned experts around the world, this holistic handbook is a toolbox for geospatial, urban, and sustainability professionals, and the artificial intelligence community"--
Handbook of geospatial approaches to sustainable cities
Weng, Qihao (editor) / Yoo, Cheolhee (editor)
2024
xix, 352 Seiten
Illustrationen, Karten, Diagramme
Enthält Literaturangaben und Index
Book
English
Achieving Sustainable Smart Cities through Geospatial Data-Driven Approaches
DOAJ | 2024
|Approaches of Flexible Spatial Planning to Sustainable Cities
TIBKAT | 2020
|Online Contents | 2012
|