Disaster Analysis using Satellite Image Data with Knowledge Transfer and Semi-Supervised Learning Techniques

With the increase in frequency of disasters and crisis situations like floods, earthquake and hurricanes, the requirement to handle the situation efficiently through disaster response and humanitarian relief has increased. Disasters are mostly unpredictable in nature with respect to their impact on people and property. Moreover, the dynamic and varied nature of disasters makes it difficult to predict their impact accurately for advanced preparation of responses [104]. It is also notable that the economical loss due to natural disasters has increased in recent years, and it, along with the pure humanitarian need, is one of the reasons to research innovative approaches to the mitigation and management of disaster operations efficiently [1]

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

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

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

Semantics-Constrained Advantageous Information Selection of Multimodal Spatiotemporal Data for Landslide Disaster Assessment

Although abundant spatiotemporal data are collected before and after landslides, the volume, variety, intercorrelation, and heterogeneity of multimodal data complicates disaster assessments, so it is challenging to select information from multimodal spatiotemporal data that is advantageous for credible and comprehensive disaster assessment. In disaster scenarios, multimodal data exhibit intrinsic relationships, and their interactions can greatly influence selection results. Previous data retrieval methods have mainly focused on candidate ranking while ignoring the generation and evaluation of candidate subsets. In this paper, a semantic-constrained data selection approach is proposed. First, multitype relationships are defined and reasoned through the heterogeneous information network. Then, relevance, redundancy, and complementarity are redefined to evaluate data sets in terms of semantic proximity and similarity. Finally, the approach is tested using Mao County (China) landslide data. The proposed method can automatically and effectively generate suitable datasets for certain tasks rather than simply ranking by similarity, and the selection results are compared with manual results to verify their effectiveness

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment