Chapter Comparisons of Deep Learning Models for Dynamic Local Climate Zone Mapping

Overview & Synopsis

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. Taking an interdisciplinary approach to sustainable cities, and with contributions from renowned experts around the world, this holistic handbook is a toolbox for geospatial, urban, and sustainability professionals, the artificial intelligence community, and those who work in related fields. Features: Explores cutting-edge geospatial and AI techniques in support of efficient, resilient, digital, and smart cities Bridges urban science and sustainability science via geospatial methods Contributes to the efforts of GEO by addressing and exemplifying pertinent societal benefit areas and engagement priorities Includes 16 case studies with a broad geographic scope that integrate societal needs with technological advances Draws expertise in geospatial technology, big data, and artificial intelligence from leading experts in the world This book is intended for researchers and scientists interested in learning techniques in GeoAI, including the technologies for collecting, analyzing, managing, processing, and visualizing geospatial datasets. Chapters 3, 6, 7, 8, and 15 of this book are freely available as a downloadable Open Access PDF at http://www.taylorfrancis.com under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.