Natural and Technological Hazards in Urban Areas

Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

Modelling of Floods in Urban Areas

This Special Issue publishes the latest advances and developments concerning the modelling of flooding in urban areas and contributes to our scientific understanding of the flooding processes and the appropriate evaluation of flood impacts. This issue contains contributions of novel methodologies including flood forecasting methods, data acquisition techniques, experimental research in urban drainage systems and/or sustainable drainage systems, and new numerical and simulation approaches in nine papers with contributions from over forty authors

Novel Approaches in Landslide Monitoring and Data Analysis

Significant progress has been made in the last few years that has expanded the knowledge of landslide processes. It is, therefore, necessary to summarize, share and disseminate the latest knowledge and expertise. This Special Issue brings together novel research focused on landslide monitoring, modelling and data analysis

Image-based recognition, 3D localization, and retro-reflectivity evaluation of high-quantity low-cost roadway assets for enhanced condition assessment

Systematic condition assessment of high-quantity low-cost roadway assets such as traffic signs, guardrails, and pavement markings requires frequent reporting on location and up-to-date status of these assets. Today, most Departments of Transportation (DOTs) in the US collect data using camera-mounted vehicles to filter, annotate, organize, and present the data necessary for these assessments. However, the cost and complexity of the collection, analysis, and reporting as-is conditions result in sparse and infrequent monitoring. Thus, some of the gains in efficiency are consumed by monitoring costs. This dissertation proposes to improve frequency, detail, and applicability of image-based condition assessment via automating detection, classification, and 3D localization of multiple types of high-quantity low-cost roadway assets using both images collected by the DOTs and online databases such Google Street View Images. To address the new requirements of US Federal Highway Administration (FHWA), a new method is also developed that simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity condition. To initiate detection and classification of high-quantity low-cost roadway assets from street-level images, a number of algorithms are proposed that automatically segment and localize high-level asset categories in 3D. The first set of algorithms focus on the task of detecting and segmenting assets at high-level categories. More specifically, a method based on Semantic Texton Forest classifiers, segments each geo-registered 2D video frame at the pixel-level based on shape, texture, and color. A Structure from Motion (SfM) procedure reconstructs the road and its assets in 3D. Next, a voting scheme assigns the most observed asset category to each point in 3D. The experimental results from application of this method are promising, nevertheless because this method relies on using supervised ground-truth pixel labels for training purposes, scaling it to various types of assets is challenging. To address this issue, a non-parametric image parsing method is proposed that leverages lazy learning scheme for segmentation and recognition of roadway assets. The semi-supervised technique used in the proposed method does not need training and provides ground truth data in a more efficient manner. It is easily scalable to thousands of video frames captured during data collection. Once the high-level asset categories are detected, specific techniques needs to be exploited to detect and classify the assets at a higher level of granularity. To this end, performance of three computer vision algorithms are evaluated for classification of traffic signs in presence of cluttered backgrounds and static and dynamic occlusions. Without making any prior assumptions about the location of traffic signs in 2D, the best performing method uses histograms of oriented gradients and color together with multiple one-vs-all Support Vector Machines, and classifies these assets into warning, regulatory, stop, and yield sign categories. To minimize the reliance on visual data collected by the DOTs and improve frequency and applicability of condition assessment, a new end-to-end procedure is presented that applies the above algorithms and creates comprehensive inventory of traffic signs using Google Street View images. By processing images extracted using Google Street View API and discriminative classification scores from all images that see a sign, the most probable 3D location of each traffic sign is derived and is shown on the Google Earth using a dynamic heat map. A data card containing information about location, type, and condition of each detected traffic sign is also created. Finally, a computer vision-based algorithm is proposed that measures retro-reflectivity of traffic signs during daytime using a vehicle mounted device. The algorithm simulates nighttime visibility of traffic signs from images taken during daytime and measures their retro-reflectivity. The technique is faster, cheaper, and safer compared to the state-of-the-art as it neither requires nighttime operation nor requires manual sign inspection. It also satisfies measurement guidelines set forth by FHWA both in terms of granularity and accuracy. To validate the techniques, new detailed video datasets and their ground-truth were generated from 2.2-mile smart road research facility and two interstate highways in the US. The comprehensive dataset contains over 11,000 annotated U.S. traffic sign images and exhibits large variations in sign pose, scale, background, illumination, and occlusion conditions. The performance of all algorithms were examined using these datasets. For retro-reflectivity measurement of traffic signs, experiments were conducted at different times of day and for different distances. Results were compared with a method recommended by ASTM standards. The experimental results show promise in scalability of these methods to reduce the time and effort required for developing road inventories, especially for those assets such as guardrails and traffic lights that are not typically considered in 2D asset recognition methods and also multiple categories of traffic signs. The applicability of Google Street View Images for inventory management purposes and also the technique for retro-reflectivity measurement during daytime demonstrate strong potential in lowering inspection costs and improving safety in practical applications

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

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 – to become ‘smart’ and ‘sustainable’. 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 ‘big’ 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 – to become ‘smart’ and ‘sustainable’. 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 ‘big’ 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

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

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 – to become ‘smart’ and ‘sustainable’. 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 ‘big’ 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