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Author: Yang Ou Publisher: ISBN: Category : Remote sensing Languages : en Pages : 164
Book Description
Four urban characteristics are identified through a review of current urban definitions. They are a) urban areas contain large and dense built-up areas; b) urban areas contain heterogeneous elements; c) urban areas are dominant by non-agricultural activities; and d) urban areas are distinguishable from their surrounding rural areas. Eight remote sensing image features are related to the urban characteristics, they are, the four proportions of vegetation, impervious surface, soil and water / shade, and the four textural features including angular second moment, inverse difference moment, contrast and entropy. They correspond to two types of information. Four proportional features correspond to land cover composition, and four textural features correspond to land cover configuration. The experiment results show that the combination of the eight features is valid for characterizing different kinds of areas and effective for distinguishing between urban and rural areas. The multi-resolution image segmentation algorithm is suitable for dividing a city region into homogeneous sub-regions that accord with the physical landscape. In the experiment of the algorithm with Landsat TM data, all the seven spectral bands show a decrease in the average grey-level range along a continuous region splitting process performed for all administrative regions of the study area. The average grey-level ranges in six of the seven bands are further reduced by removing the administrative boundary constraint. An urban area is successfully recognized through an iterative clustering and merging process, performed on the homogeneous regions output from the image segmentation process with the eight proportional and textual features. An experiment shows that the iterative clustering and identification is able to identify an area that can be definitely labelled as urban. Another experiment shows that the iterative merging process is able to identify the urban and rural areas of a city region with the maximum distance between them in the feature space. The resulting urban area is evaluated by a fact consistency checking. By overlapping the resulting urban area with some referenced data, it is verified that all the facts identified about the study area are satisfied by the recognition result.
Author: Yang Ou Publisher: ISBN: Category : Remote sensing Languages : en Pages : 164
Book Description
Four urban characteristics are identified through a review of current urban definitions. They are a) urban areas contain large and dense built-up areas; b) urban areas contain heterogeneous elements; c) urban areas are dominant by non-agricultural activities; and d) urban areas are distinguishable from their surrounding rural areas. Eight remote sensing image features are related to the urban characteristics, they are, the four proportions of vegetation, impervious surface, soil and water / shade, and the four textural features including angular second moment, inverse difference moment, contrast and entropy. They correspond to two types of information. Four proportional features correspond to land cover composition, and four textural features correspond to land cover configuration. The experiment results show that the combination of the eight features is valid for characterizing different kinds of areas and effective for distinguishing between urban and rural areas. The multi-resolution image segmentation algorithm is suitable for dividing a city region into homogeneous sub-regions that accord with the physical landscape. In the experiment of the algorithm with Landsat TM data, all the seven spectral bands show a decrease in the average grey-level range along a continuous region splitting process performed for all administrative regions of the study area. The average grey-level ranges in six of the seven bands are further reduced by removing the administrative boundary constraint. An urban area is successfully recognized through an iterative clustering and merging process, performed on the homogeneous regions output from the image segmentation process with the eight proportional and textual features. An experiment shows that the iterative clustering and identification is able to identify an area that can be definitely labelled as urban. Another experiment shows that the iterative merging process is able to identify the urban and rural areas of a city region with the maximum distance between them in the feature space. The resulting urban area is evaluated by a fact consistency checking. By overlapping the resulting urban area with some referenced data, it is verified that all the facts identified about the study area are satisfied by the recognition result.
Author: Basudeb Bhatta Publisher: Springer Science & Business Media ISBN: 3642052991 Category : Science Languages : en Pages : 191
Book Description
This book provides a comprehensive discussion on urban growth and sprawl, and how they can be analyzed using remote sensing imageries. It compiles views of numerous researchers that help in understanding the urban growth and sprawl; their patterns, process, causes, consequences, and countermeasures; how remote sensing data and geographic information system techniques can be used in mapping, monitoring, measuring, analyzing, and simulating the urban growth and sprawl and what are the merits and demerits of available methods and models. This book will be of value for the scientists and researchers engaged in urban geographic research, especially using remote sensing imageries. This book will serve as a rigours literature review for them. Post graduate students of urban geography or urban/regional planning may refer this book as additional studies. This book may help the academicians for preparing lecture notes and delivering lectures. Industry professionals may also be benefited from the discussed methods and models along with numerous citations.
Author: Nicola Colaninno Publisher: ISBN: Category : Languages : en Pages : 454
Book Description
From a global point of view, as argued by Levy (1999), the modern city has undergone radical changes in its physical form, either in terms of territorial expansion as well as in terms of interna! physical transformations. Today, approximately 75% of the European population lives in urban areas ,which makes the urban fulure of the conlinent a major cause of concern (Brazil, Cavalcanti, & Longo, 2014). lndeed, the demand for urban land, both within and around the cities, is becoming increasingly acule (European Environmenl Agency, 2006). Ouring the last decades, also Spain has been undergoing an important process of urban growth, which has implied the consumption of a large amounl of land, al hough the overall population growth rale, mostly along certain specific geographic areas, has remained at least unchanged or even, in sorne cases, il has also decreased. Such a phenomenon has been quite remarkable along the Mediterranean side. As argued by Gaja (2008), the urban development in Spain has been strongly linked to the model of economic development , which relies, since its launch in the 50's, onlhree main factors , i.e.:emigration, building, and mass tourism. Nowadays , in Spain, and mostly along the Medilerranean side, several urban areas are facing important phenomena of urban sprawl, also feared by he European Union. An accurate information about the pattern of land use/land cover, over time, is a fundamental requirement for a better understanding of the urban models. Currently, even though plenty of approaches to the image classification, through Remote Sensing (RS) techniques, have been advanced, Land Cover/Land Use classification is still an exciting challenge (Weng, 2010). Actually, the increasing development of RS and GIS technologies, during the last decades, has provided further capabiliies for measuring, analysing, understanding, modelling the "physical expressions" of urban growth phenomena, either in terms of pattern and process (Bhatta, 2012), and based on land use/land cover mapping and change delection over time. Based on such a technological approach, here we first aim to set up a suitable methodology for detecting generalized land cover classes based on an assisted automatic (or semi-aulomatic) pixel-based approach, calibrated upon Landsat Thematic Mapper (TM) mullispectral imagery, at 30 meters of spatial resolution. Beside, through the use of Geographical lnformation Syslem (GIS) we provide a spatial analysis and modelling of different urban models, from a morphological standpoint, in order to define the main pattern of land occupation al municipal scale, and along the Mediterranean side of Spain, al the year 2011. We focus on two main issues. On one hand, RS techniques have been used to set up a proper semi-automatic classification methodology, based on the use of Landsat imagery, capable of handling huge geographical areas quickly and efficiently. This process is basically aimed to detect the urban areas, at the year 2011, along the Mediterranean side of Spain, depending on the administrative division of Autonomous Communities. On the other hand, the spatial patterns of urban settlements have been analysed by using a GIS platform for quantifying a set of spatial metrics about the urban form. Hence, once get the quantification of different morphological features, including the analysis aboul either the urban profile, the urban texture, and the street network pattern, an automatic classification of different urban morphological models has been proposed, based on a stalistical approaches, namely factor and cluster analysis.
Author: United Nations Publications Publisher: ISBN: 9789211483185 Category : Political Science Languages : en Pages : 34
Book Description
This report presents the highlights of the 2018 Revision of World Urbanization Prospects, which contains the latest estimates of the urban and rural populations or areas from 1950 to 2018 and projections to 2050, as well as estimates of population size from 1950 to 2018 and projections to 2030 for all urban agglomerations with 300,000 inhabitants or more in 2018. The world urban population is at an all-time high, and the share of urban dwellers, is projected to represent two thirds of the global population in 2050. Continued urbanization will bring new opportunities and challenges for sustainable development.
Author: Jiao Jin Publisher: ISBN: Category : Languages : en Pages : 134
Book Description
Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images. The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers.
Author: Qihao Weng Publisher: CRC Press ISBN: 1420008803 Category : Technology & Engineering Languages : en Pages : 450
Book Description
Driven by advances in technology and societal needs, the next frontier in remote sensing is urban areas. With the advent of high-resolution imagery and more capable techniques, the question has become "Now that we have the technology, how do we use it?" The need for a definitive resource that explores the technology of remote sensing and the issues it can resolve in an urban setting has never been more acute. Containing contributions from world renowned experts, Urban Remote Sensing provides a review of basic concepts, methodologies, and case studies. Each chapter demonstrates how to apply up-to-date techniques to the problems identified and how to analyze research results. Organized into five sections, this book: Focuses on data, sensors, and systems considerations as well as algorithms for urban feature extraction Analyzes urban landscapes in terms of composition and structure, especially using sub-pixel analysis techniques Presents methods for monitoring, analyzing, and modeling urban growth Illustrates various approaches to urban planning and socio-economic applications of urban remote sensing Assesses the progress made to date, identifies the existing problems and challenges, and demonstrates new developments and trends in urban remote sensing This book is ideal for upper division undergraduate and graduate students, however it can also serve as a reference for researchers or those individuals interested in the remote sensing of cities in academia, and governmental and commercial sectors. Urban Remote Sensing examines how to apply remote sensing technology to urban and suburban areas.
Author: Mohammad Rezaee Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
The incorporation of spatial and spectral information within multispectral satellite images is the key for accurate land cover mapping, specifically for discrimination of heterogeneous land covers. Traditional methods only use basic features, either spatial features (e.g. edges or gradients) or spectral features (e.g. mean value of Digital Numbers or Normalized Difference Vegetation Index (NDVI)) for land cover classification. These features are called low level features and are generated manually (through so-called feature engineering). Since feature engineering is manual, the design of proper features is time-consuming, only low-level features in the information hierarchy can usually be extracted, and the feature extraction is application-based (i.e., different applications need to extract different features). In contrast to traditional land-cover classification methods, Deep Learning (DL),adapting the artificial neural network (ANN) into a deep structure, can automatically generate the necessary high-level features for improving classification without being limited to low-level features. The higher-level features (e.g. complex shapes and textures) can be generated by combining low-level features through different level of processing. However, despite recent advances of DL for various computer vision tasks, especially for convolutional neural networks (CNNs) models, the potential of using DL for land-cover classification of multispectral remote sensing (RS) images have not yet been thoroughly explored. The main reason is that a DL network needs to be trained using a huge number of images from a large scale of datasets. Such training datasets are not usually available in RS. The only few available training datasets are either for object detection in an urban area, or for scene labeling. In addition, the available datasets are mostly used for land-cover classification based on spatial features. Therefore, the incorporation of the spectral and spatial features has not been studied comprehensively yet. This PhD research aims to mitigate challenges in using DL for RS land cover mapping/object detection by (1) decreasing the dependency of DL to the large training datasets, (2) adapting and improving the efficiency and accuracy of deep CNNs for heterogeneous classification, (3) incorporating all of the spectral bands in satellite multispectral images into the processing, and (4) designing a specific CNN network that can be used for a faster and more accurate detection of heterogeneous land covers with fewer amount of training datasets. The new developments are evaluated in two case studies, i.e. wetland detection and tree species detection, where high resolution multispectral satellite images are used. Such land-cover classifications are considered as challenging tasks in the literature. The results show that our new solution works reliably under a wide variety of conditions. Furthermore, we are releasing the two large-scale wetland and tree species detection datasets to the public in order to facilitate future research, and to compare with other methods.
Author: Vivienne Sze Publisher: Springer Nature ISBN: 3031017668 Category : Technology & Engineering Languages : en Pages : 254
Book Description
This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Author: Manfred Ehlers Publisher: SPIE-International Society for Optical Engineering ISBN: Category : Science Languages : en Pages : 510
Book Description
Proceedings of SPIE present the original research papers presented at SPIE conferences and other high-quality conferences in the broad-ranging fields of optics and photonics. These books provide prompt access to the latest innovations in research and technology in their respective fields. Proceedings of SPIE are among the most cited references in patent literature.
Author: Yang Ou
Author: Yang Ou
Author: Basudeb Bhatta
Author: Nicola Colaninno
Author: United Nations Publications
Author: Jiao Jin
Author: Qihao Weng
Author: Mohammad Rezaee
Author: Vivienne Sze
Author: Jian Chen
Author: Manfred Ehlers