17 research outputs found
Advances in Image Processing, Analysis and Recognition Technology
For many decades, researchers have been trying to make computersâ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches
Synthetic Aperture Radar (SAR) Meets Deep Learning
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
Remote Sensing
This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas
LIDAR based semi-automatic pattern recognition within an archaeological landscape
LIDAR-Daten bieten einen neuartigen Ansatz zur Lokalisierung und Ăberwachung des kulturellen Erbes in der Landschaft, insbesondere in schwierig zu erreichenden Gebieten, wie im Wald, im unwegsamen GelĂ€nde oder in sehr abgelegenen Gebieten. Die manuelle Lokalisation und Kartierung von archĂ€ologischen Informationen einer Kulturlandschaft ist in der herkömmlichen Herangehensweise eine sehr zeitaufwĂ€ndige Aufgabe des Fundstellenmanagements (Cultural Heritage Management). Um die Möglichkeiten in der Erkennung und bei der Verwaltung des kulturellem Erbes zu verbessern und zu ergĂ€nzen, können computergestĂŒtzte Verfahren einige neue LösungsansĂ€tze bieten, die darĂŒber hinaus sogar die Identifizierung von fĂŒr das menschliche Auge bei visueller Sichtung nicht erkennbaren Details ermöglichen. Aus archĂ€ologischer Sicht ist die vorliegende Dissertation dadurch motiviert, dass sie LIDAR-GelĂ€ndemodelle mit archĂ€ologischen Befunden durch automatisierte und semiautomatisierte Methoden zur Identifizierung weiterer archĂ€ologischer Muster zu Bodendenkmalen als digitale âLIDAR-Landschaftâ bewertet. Dabei wird auf möglichst einfache und freie verfĂŒgbare algorithmische AnsĂ€tze (Open Source) aus der Bildmustererkennung und Computer Vision zur Segmentierung und Klassifizierung der LIDAR-Landschaften zur groĂflĂ€chigen Erkennung archĂ€ologischer DenkmĂ€ler zurĂŒckgegriffen. Die Dissertation gibt dabei einen umfassenden Ăberblick ĂŒber die archĂ€ologische Nutzung und das Potential von LIDAR-Daten und definiert anhand qualitativer und quantitativer AnsĂ€tze den Entwicklungsstand der semiautomatisierten Erkennung archĂ€ologischer Strukturen im Rahmen archĂ€ologischer Prospektion und Fernerkundungen. DarĂŒber hinaus erlĂ€utert sie Best Practice-Beispiele und den einhergehenden aktuellen Forschungsstand. Und sie veranschaulicht die QualitĂ€t der Erkennung von BodendenkmĂ€lern durch die semiautomatisierte Segmentierung und Klassifizierung visualisierter LIDAR-Daten. Letztlich identifiziert sie das Feld fĂŒr weitere Anwendungen, wobei durch eigene, algorithmische Template Matching-Verfahren groĂflĂ€chige Untersuchungen zum kulturellen Erbe ermöglicht werden. ResĂŒmierend vergleicht sie die analoge und computergestĂŒtzte Bildmustererkennung zu Bodendenkmalen, und diskutiert abschlieĂend das weitere Potential LIDAR-basierter Mustererkennung in archĂ€ologischen Kulturlandschaften
Methodology for high resolution spatial analysis of the physical flood susceptibility of buildings in large river floodplains
The impacts of floods on buildings in urban areas are increasing due to the intensification of extreme weather events, unplanned or uncontrolled settlements and the rising vulnerability of assets. There are some approaches available for assessing the flood damage to buildings and critical infrastructure. To this point, however, it is extremely difficult to adapt these methods widely, due to the lack of high resolution classification and characterisation approaches for built structures. To overcome this obstacle, this work presents: first, a conceptual framework for understanding the physical flood vulnerability and the physical flood susceptibility of buildings, second, a methodological framework for the combination of methods and tools for a large-scale and high-resolution analysis and third, the testing of the methodology in three pilot sites with different development conditions.
The conceptual framework narrows down an understanding of flood vulnerability, physical flood vulnerability and physical flood susceptibility and its relation to social and economic vulnerabilities. It describes the key features causing the physical flood susceptibility of buildings as a component of the vulnerability. The methodological framework comprises three modules: (i) methods for setting up a building topology, (ii) methods for assessing the susceptibility of representative buildings of each building type and (iii) the integration of the two modules with technological tools.
The first module on the building typology is based on a classification of remote sensing data and GIS analysis involving seven building parameters, which appeared to be relevant for a classification of buildings regarding potential flood impacts. The outcome is a building taxonomic approach. A subsequent identification of representative buildings is based on statistical analyses and membership functions.
The second module on the building susceptibility for representative buildings bears on the derivation of depth-physical impact functions. It relates the principal building components, including their heights, dimensions and materials, to the damage from different water levels. The materialâs susceptibility is estimated based on international studies on the resistance of building materials and a fuzzy expert analysis. Then depth-physical impact functions are calculated referring to the principal components of the buildings which can be affected by different water levels. Hereby, depth-physical impact functions are seen as a means for the interrelation between the water level and the physical impacts.
The third module provides the tools for implementing the methodology. This tool compresses the architecture for feeding the required data on the buildings with their relations to the building typology and the building-type specific depth-physical impact function supporting the automatic process.
The methodology is tested in three flood plains pilot sites: (i) in the settlement of the Barrio Sur in Magangué and (ii) in the settlement of La Peña in Cicuco located on the flood plain of Magdalena River, Colombia and (iii) in a settlement of the city of Dresden, located on the Elbe River, Germany. The testing of the methodology covers the description of data availability and accuracy, the steps for deriving the depth-physical impact functions of representative buildings and the final display of the spatial distribution of the physical flood susceptibility.
The discussion analyses what are the contributions of this work evaluating the findings of the methodologyâs testing with the dissertation goals. The conclusions of the work show the contributions and limitations of the research in terms of methodological and empirical advancements and the general applicability in flood risk management.:1 INTRODUCTION 1
1.1 Background 1
1.2 State of the art 2
1.3 Problem statement 6
1.4 Objectives 6
1.5 Approach and outline 6
2 CONCEPTUAL FRAMEWORK 9
2.1 Flood vulnerability 10
2.2 Physical flood vulnerability 12
2.3 Physical flood susceptibility 14
3 METHODOLOGICAL FRAMEWORK 23
3.1 Module 1: Building taxonomy for settlements 24
3.1.1 Extraction of building features 24
3.1.2 Derivation of building parameters for setting up a building taxonomy 38
3.1.3 Selection of representative buildings for a building susceptibility assessment 51
3.2 Module 2: Physical susceptibility of representative buildings 57
3.2.1 Identification of building components 57
3.2.2 Qualification of building material susceptibility 62
3.2.3 Derivation of a depth-physical impact function 71
3.3 Module 3: Technological integration 77
3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77
3.3.2 Tools supporting the physical susceptibility analysis 78
3.3.3 The users and their requirements 79
4 RESULTS OF THE METHODOLOGY TESTING 83
4.1 Pilot site âKleinzschachwitzâ â Dresden, Germany â Elbe River 83
4.1.1 Module 1: Building taxonomy â âKleinzschachwitzâ 85
4.1.2 Module 2: Physical susceptibility of representative buildings â âKleinzschachwitzâ 97
4.1.3 Module 3: Technological integration â âKleinzschachwitzâ 103
4.2 Pilot site âLa Peñaâ â Cicuco, Colombia â Magdalena River 107
4.2.1 Module 1: Building taxonomy â âLa Peñaâ 108
4.2.2 Module 2: Physical susceptibility of representative buildings â âLa Peñaâ 121
4.2.3 Module 3: Technological integrationâ âLa Peñaâ 129
4.3 Pilot site âBarrio Surâ â MaganguĂ©, Colombia â Magdalena River 133
4.3.1 Module 1: Building taxonomy â âBarrio Surâ 133
4.3.2 Module 2: Physical susceptibility of representative buildings â âBarrio Surâ 141
4.3.3 Module 3: Technological integration â âBarrio Surâ 147
4.4 Empirical findings 151
4.4.1 Empirical findings of Module 1 151
4.4.2 Empirical findings of Module 2 155
4.4.3 Empirical findings of Module 3 157
4.4.4 Guidance of the methodology 157
5 DISCUSSION 161
5.1 Discussion on the conceptual framework 161
5.2 Discussion on the methodological framework 161
5.2.1 Discussion on Module 1: the building taxonomic approach 162
5.2.2 Discussion on Module 2: the depth-physical impact function 164
6 CONCLUSIONS AND OUTLOOK 167
6.1 Conclusions 167
6.2 Outlook 168
REFERENCES 171
INDEX OF FIGURES 199
INDEX OF TABLES 201
APPENDICES 203In vielen StĂ€dten nehmen die Auswirkungen von Hochwasser auf GebĂ€ude aufgrund immer extremerer Wetterereignisse, unkontrollierbarer Siedlungsbauten und der steigenden VulnerabilitĂ€t von BesitztĂŒmern stetig zu. Es existieren zwar bereits AnsĂ€tze zur Beurteilung von WasserschĂ€den an GebĂ€uden und Infrastrukturknotenpunkten. Doch ist es bisher schwierig, diese Methoden groĂrĂ€umig anzuwenden, da es an einer prĂ€zisen Klassifizierung und Charakterisierung von GebĂ€uden und anderen baulichen Anlagen fehlt. Zu diesem Zweck sollen in dieser Arbeit erstens ein Konzept fĂŒr ein genaueres VerstĂ€ndnis der physischen VulnerabilitĂ€t von GebĂ€uden gegenĂŒber Hochwasser dargelegt, zweitens ein methodisches Verfahren zur Kombination der bestehenden Methoden und Hilfsmittel mit dem Ziel einer groĂrĂ€umigen und hochauflösenden Analyse erarbeitet und drittens diese Methode an drei Pilotstandorten mit unterschiedlichem Ausbauzustand erprobt werden.
Die Rahmenbedingungen des Konzepts grenzen die Begriffe der VulnerabilitĂ€t, der physischen VulnerabilitĂ€t und der physischen AnfĂ€lligkeit gegenĂŒber Hochwasser ein und erörtern deren Beziehung zur sozialen und ökonomischen VulnerabilitĂ€t. Es werden die Merkmale der physischen AnfĂ€lligkeit von GebĂ€uden gegenĂŒber Hochwasser als Bestandteil der VulnerabilitĂ€t definiert. Das methodische Verfahren umfasst drei Module: (i) Methoden zur Erstellung einer GebĂ€udetypologie, (ii) Methoden zur Bewertung der AnfĂ€lligkeit reprĂ€sentativer GebĂ€ude jedes GebĂ€udetyps und (iii) die Kombination der beiden Module mit Hilfe technologischer Hilfsmittel.
Das erste Modul zur GebĂ€udetypologie basiert auf der Klassifizierung von Fernerkundungsdaten und GIS-Analysen anhand von sieben GebĂ€udeparametern, die sich fĂŒr die Klassifizierung von GebĂ€uden bezĂŒglich ihres Risikopotenzials bei Hochwasser als wichtig erweisen. Daraus ergibt sich ein Ansatz zur GebĂ€udeklassifizierung. Die anschlieĂende Ermittlung reprĂ€sentativer GebĂ€ude beruht auf statistischen Analysen und Zugehörigkeitsfunktionen.
Das zweite Modul zur AnfĂ€lligkeit reprĂ€sentativer GebĂ€ude beruht auf der Ableitung von Funktion von Wasserstand und physischer Einwirkung. Es setzt die relevanten GebĂ€udemerkmale, darunter Höhe, MaĂe und Materialien, in Beziehung zum erwartbaren Schaden bei unterschiedlichen WasserstĂ€nden. Die MaterialanfĂ€lligkeit wird aufgrund internationaler Studien zur Festigkeit von Baustoffen sowie durch Anwendung eines Fuzzy-Logic-Expertensystems eingeschĂ€tzt. AnschlieĂend werden Wasserstand-Schaden-Funktionen unter Einbeziehung der HauptgebĂ€udekomponenten berechnet, die durch unterschiedliche WasserstĂ€nde in Mitleidenschaft gezogen werden können. Funktion von Wasserstand und physischer Einwirkung dienen hier dazu, den jeweiligen Wasserstand und die physischen Auswirkung in Beziehung zueinander zu setzen.
Das dritte Modul stellt die zur Umsetzung der Methoden notwendigen Hilfsmittel vor. Zur UnterstĂŒtzung des automatisierten Verfahrens dienen Hilfsmittel, die die GebĂ€udetypologie mit der Funktion von Wasserstand und physischer Einwirkung fĂŒr GebĂ€ude in Hochwassergebieten kombinieren.
Die Methoden wurden anschlieĂend in drei hochwassergefĂ€hrdeten Pilotstandorten getestet: (i) in den Siedlungsgebieten von Barrio Sur in MaganguĂ© und (ii) von La Pena in Cicuco, zwei Ăberschwemmungsgebiete des Magdalenas in Kolumbien, und (iii) im Stadtgebiet von Dresden, das an der Elbe liegt. Das Testverfahren umfasst die Beschreibung der DatenverfĂŒgbarkeit und genauigkeit, die einzelnen Schritte zur Analyse der. Funktion von Wasserstand und physischer Einwirkung reprĂ€sentativer GebĂ€ude sowie die Darstellung der rĂ€umlichen Verteilung der physischen AnfĂ€lligkeit fĂŒr Hochwasser.
In der Diskussion wird der Beitrag dieser Arbeit zur Beurteilung der Erkenntnisse der getesteten Methoden anhand der Ziele dieser Dissertation analysiert. Die Folgerungen beleuchten abschlieĂend die Fortschritte und auch Grenzen der Forschung hinsichtlich methodischer und empirischer Entwicklungen sowie deren allgemeine Anwendbarkeit im Bereich des Hochwasserschutzes.:1 INTRODUCTION 1
1.1 Background 1
1.2 State of the art 2
1.3 Problem statement 6
1.4 Objectives 6
1.5 Approach and outline 6
2 CONCEPTUAL FRAMEWORK 9
2.1 Flood vulnerability 10
2.2 Physical flood vulnerability 12
2.3 Physical flood susceptibility 14
3 METHODOLOGICAL FRAMEWORK 23
3.1 Module 1: Building taxonomy for settlements 24
3.1.1 Extraction of building features 24
3.1.2 Derivation of building parameters for setting up a building taxonomy 38
3.1.3 Selection of representative buildings for a building susceptibility assessment 51
3.2 Module 2: Physical susceptibility of representative buildings 57
3.2.1 Identification of building components 57
3.2.2 Qualification of building material susceptibility 62
3.2.3 Derivation of a depth-physical impact function 71
3.3 Module 3: Technological integration 77
3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77
3.3.2 Tools supporting the physical susceptibility analysis 78
3.3.3 The users and their requirements 79
4 RESULTS OF THE METHODOLOGY TESTING 83
4.1 Pilot site âKleinzschachwitzâ â Dresden, Germany â Elbe River 83
4.1.1 Module 1: Building taxonomy â âKleinzschachwitzâ 85
4.1.2 Module 2: Physical susceptibility of representative buildings â âKleinzschachwitzâ 97
4.1.3 Module 3: Technological integration â âKleinzschachwitzâ 103
4.2 Pilot site âLa Peñaâ â Cicuco, Colombia â Magdalena River 107
4.2.1 Module 1: Building taxonomy â âLa Peñaâ 108
4.2.2 Module 2: Physical susceptibility of representative buildings â âLa Peñaâ 121
4.2.3 Module 3: Technological integrationâ âLa Peñaâ 129
4.3 Pilot site âBarrio Surâ â MaganguĂ©, Colombia â Magdalena River 133
4.3.1 Module 1: Building taxonomy â âBarrio Surâ 133
4.3.2 Module 2: Physical susceptibility of representative buildings â âBarrio Surâ 141
4.3.3 Module 3: Technological integration â âBarrio Surâ 147
4.4 Empirical findings 151
4.4.1 Empirical findings of Module 1 151
4.4.2 Empirical findings of Module 2 155
4.4.3 Empirical findings of Module 3 157
4.4.4 Guidance of the methodology 157
5 DISCUSSION 161
5.1 Discussion on the conceptual framework 161
5.2 Discussion on the methodological framework 161
5.2.1 Discussion on Module 1: the building taxonomic approach 162
5.2.2 Discussion on Module 2: the depth-physical impact function 164
6 CONCLUSIONS AND OUTLOOK 167
6.1 Conclusions 167
6.2 Outlook 168
REFERENCES 171
INDEX OF FIGURES 199
INDEX OF TABLES 201
APPENDICES 203El impacto de las inundaciones sobre los edificios en zonas urbanas es cada vez mayor debido a la intensificaciĂłn de los fenĂłmenos meteorolĂłgicos extremos, asentamientos no controlados o no planificados y su creciente vulnerabilidad. Hay mĂ©todos disponibles para evaluar los daños por inundaciĂłn en edificios e infraestructuras crĂticas. Sin embargo, es muy difĂcil implementar estos mĂ©todos sistemĂĄticamente en grandes ĂĄreas debido a la falta de clasificaciĂłn y caracterizaciĂłn de estructuras construidas en resoluciones detalladas. Para superar este obstĂĄculo, este trabajo se enfoca, en primer lugar, en desarrollar un marco conceptual para comprender la vulnerabilidad y susceptibilidad fĂsica de edificios por inudaciones, en segundo lugar, en desarrollar un marco metodolĂłgico para la combinaciĂłn de los mĂ©todos y herramientas para una anĂĄlisis de alta resoluciĂłn y en tercer lugar, la prueba de la metodologĂa en tres sitios experimentales, con distintas condiciones de desarrollo.
El marco conceptual se enfoca en comprender la vulnerabilidad y susceptibility de las edificaciones frente a inundaciones, y su relaciĂłn con la vulnerabilidad social y econĂłmica. En Ă©l se describen las principales caracterĂsticas fĂsicas de la susceptibilidad de edificicaiones como un componente de la vulnerabilidad. El marco metodolĂłgico consta de tres mĂłdulos: (i) mĂ©todos para la derivaciĂłn de topologĂa de construcciones, (ii) mĂ©todos para evaluar la susceptibilidad de edificios representativos y (iii) la integraciĂłn de los dos mĂłdulos a travĂ©s herramientas tecnolĂłgicas.
El primer mĂłdulo de topologĂa de construcciones se basa en una clasificaciĂłn de datos de sensoramiento rĂ©moto y procesamiento SIG para la extracciĂłn de siete parĂĄmetros de las edficaciones. Este mĂłdulo parece ser aplicable para una clasificaciĂłn de los edificios en relaciĂłn con los posibles impactos de las inundaciones. El resultado es una taxonomĂa de las edificaciones y una posterior identificaciĂłn de edificios representativos que se basa en anĂĄlisis estadĂsticos y funciones de pertenencia.
El segundo mĂłdulo consiste en el anĂĄlisis de susceptibilidad de las construcciones representativas a travĂ©s de funciones de profundidad del impacto fĂsico. Las cuales relacionan los principales componentes de la construcciĂłn, incluyendo sus alturas, dimensiones y materiales con los impactos fĂsicos a diferentes niveles de agua. La susceptibilidad del material se calcula con base a estudios internacionales sobre la resistencia de los materiales y un anĂĄlisis a travĂ©s de sistemas expertos difusos. AquĂ, las funciones de profundidad de impacto fĂsico son considerados como un medio para la interrelaciĂłn entre el nivel del agua y los impactos fĂsicos.
El tercer mĂłdulo proporciona las herramientas necesarias para la aplicaciĂłn de la metodologĂa. Estas herramientas tecnolĂłgicas consisten en la arquitectura para la alimentaciĂłn de los datos relacionados a la tipologĂa de construcciones con las funciones de profundidad del impacto fĂsico apoyado en procesos automĂĄticos.
La metodologĂa es probada en tres sitios piloto: (i) en el Barrio Sur en MaganguĂ© y (ii) en la barrio de La Peña en Cicuco situado en la llanura inundable del RĂo Magdalena, Colombia y (iii) en barrio Kleinzschachwitz de la ciudad de Dresden, situado a orillas del rĂo Elba, en Alemania. Las pruebas de la metodologĂa abarca la descripciĂłn de la disponibilidad de los datos y la precisiĂłn, los pasos a seguir para obtener las funciones profundidad de impacto fĂsico de edificios representativos y la presentaciĂłn final de la distribuciĂłn espacial de la susceptibilidad fĂsica frente inundaciones
El discusiĂłn analiza las aportaciones de este trabajo y evalua los resultados de la metodologĂa con relaciĂłn a los objetivos. Las conclusiones del trabajo, muestran los aportes y limitaciones de la investigaciĂłn en tĂ©rminos de avances metodolĂłgicos y empĂricos y la aplicabilidad general de gestiĂłn del riesgo de inundaciones.:1 INTRODUCTION 1
1.1 Background 1
1.2 State of the art 2
1.3 Problem statement 6
1.4 Objectives 6
1.5 Approach and outline 6
2 CONCEPTUAL FRAMEWORK 9
2.1 Flood vulnerability 10
2.2 Physical flood vulnerability 12
2.3 Physical flood susceptibility 14
3 METHODOLOGICAL FRAMEWORK 23
3.1 Module 1: Building taxonomy for settlements 24
3.1.1 Extraction of building features 24
3.1.2 Derivation of building parameters for setting up a building taxonomy 38
3.1.3 Selection of representative buildings for a building susceptibility assessment 51
3.2 Module 2: Physical susceptibility of representative buildings 57
3.2.1 Identification of building components 57
3.2.2 Qualification of building material susceptibility 62
3.2.3 Derivation of a depth-physical impact function 71
3.3 Module 3: Technological integration 77
3.3.1 Combination of the depth-physical impact function with the building taxonomic code 77
3.3.2 Tools supporting the physical susceptibility analysis 78
3.3.3 The users and their requirements 79
4 RESULTS OF THE METHODOLOGY TESTING 83
4.1 Pilot site âKleinzschachwitzâ â Dresden, Germany â Elbe River 83
4.1.1 Module 1: Building taxonomy â âKleinzschachwitzâ 85
4.1.2 Module 2: Physical susceptibility of representative buildings â âKleinzschachwitzâ 97
4.1.3 Module 3: Technological integration â âKleinzschachwitzâ 103
4.2 Pilot site âLa Peñaâ â Cicuco, Colombia â Magdalena River 107
4.2.1 Module 1: Building taxonomy â âLa Peñaâ 108
4.2.2 Module 2: Physical susceptibility of representative buildings â âLa Peñaâ 121
4.2.3 Module 3: Technological integrationâ âLa Peñaâ 129
4.3 Pilot site âBarrio Surâ â MaganguĂ©, Colombia â Magdalena River 133
4.3.1 Module 1: Building taxonomy â âBarrio Surâ 133
4.3.2 Module 2: Physical susceptibility of representative buildings â âBarrio Surâ 141
4.3.3 Module 3: Technological integration â âBarrio Surâ 147
4.4 Empirical findings 151
4.4.1 Empirical findings of Module 1 151
4.4.2 Empirical findings of Module 2 155
4.4.3 Empirical findings of Module 3 157
4.4.4 Guidance of the methodology 157
5 DISCUSSION 161
5.1 Discussion on the conceptual framework 161
5.2 Discussion on the methodological framework 161
5.2.1 Discussion on Module 1: the building taxonomic approach 162
5.2.2 Discussion on Module 2: the depth-physical impact function 164
6 CONCLUSIONS AND OUTLOOK 167
6.1 Conclusions 167
6.2 Outlook 168
REFERENCES 171
INDEX OF FIGURES 199
INDEX OF TABLES 201
APPENDICES 20
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently â to become âsmartâ and âsustainableâ. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of âbigâ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently â to become âsmartâ and âsustainableâ. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of âbigâ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently â to become âsmartâ and âsustainableâ. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of âbigâ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity