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This book analyses the implications of eco-urbanism re-making for policy and practice under the transformational trends of economic decentralization and market reform in China. While the guiding themes are space, scale, and governance of cities, the book focuses on three interrelated prevailing processes of local green space reproduction, cross-scale mediation of eco-city planning ideology and mobilized social-economic-political intricacies among different countries. This book addresses the ongoing global diffusion and diversification of sustainable urbanism discourses, debates and practices to portray, evaluate, remake and implement a sustainable form of urban development, using China as a national example. As eco-city practice becomes a city-branding instrument worldwide, this new urban development vision is also well embraced by Chinese local governments. In these contexts, the Chinese government has initiated and endorsed a number of massive projects to promote green urbanism, steering urbanization onto a more sustainable trajectory. The construction of these “ecotopias” involves a multitude of processes ranging from policy transfer/mobility to institutional design, from innovation in green technologies to the promotion of green buildings, and from policy implementation to public participation.

Sustainable urban development. --- Environmentally sustainable urban development --- City planning --- Sustainable development --- Sociology, Urban. --- Urban Geography / Urbanism (inc. megacities, cities, towns). --- Urban Studies/Sociology. --- Urban sociology --- Cities and towns

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This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports.

Marine pollution. --- Marine environment pollution --- Marine water pollution --- Ocean pollution --- Offshore water pollution --- Sea pollution --- Seawater --- Coastal zone management --- Oceanography --- Pollution --- Water --- Marine resources conservation