Machine Learning and Remote Sensing for Near Realtime Flood Mapping PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Machine Learning and Remote Sensing for Near Realtime Flood Mapping PDF full book. Access full book title Machine Learning and Remote Sensing for Near Realtime Flood Mapping by Bo Peng. Download full books in PDF and EPUB format.
Author: Bo Peng
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Flooding has been the world most frequent natural disaster over the past decades. Near realtime flood mapping is critical for mitigating the devastating impacts of flooding (e.g., life/economic losses). The United Nations (UN) Sustainable Development Goal 11 (2015-2030) is calling for global efforts to monitor large-scale floods in near realtime for protecting the vulnerable communities. Remote sensing (RS) has the unique capability of earth observation with applications in global disaster response. Recent advances in micro-satellite constellations are offering opportunities for daily satellite image acquisition with a high spatial resolution. Such a high temporal resolution (i.e., daily) is the key to the success of RS for near realtime global flood monitoring. Moreover, the large volume of RS imagery further unleashes the vast potential of data-driven machine learning (ML) approaches for accurate flood mapping. Recent developments in deep learning, a sub-field of ML, have demonstrated the superiority of ML in flood mapping given massive RS data and corresponding labels. Unfortunately, training labels are very scarce in the domain of disaster response. This dissertation explores ML models for flood mapping without massive training labels by introducing the prior knowledge in RS data including the spatial, temporal, and spectral information: First, this dissertation proposes the PSNet to model flood mapping as bitemporal change detection by leveraging the temporal information of RS data. With only a few hand labels, the model can map floods with an over 0.85 F1 score for various heterogeneous flooded areas. Second, to further accelerate flood mapping, a self-supervised bitemporal change detection model, Patch-CVA, is developed to detect changed areas without hand labels. Further post-processing removes most false alarms by introducing the spatial and spectral information of floodwaters. Third, while the Patch-CVA has achieved good performance, its underlying assumption (i.e., most changes are flooded areas) may not hold in certain environments (e.g., crop growth). This dissertation proposes a weak label generation framework by fusing the spatial, temporal, and spectral information of geospatial data. A large weakly labeled dataset is obtained to train data-driven ML models for pixel-wise flood mapping with an over 0.9 F1 score.
Author: Bo Peng
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
Flooding has been the world most frequent natural disaster over the past decades. Near realtime flood mapping is critical for mitigating the devastating impacts of flooding (e.g., life/economic losses). The United Nations (UN) Sustainable Development Goal 11 (2015-2030) is calling for global efforts to monitor large-scale floods in near realtime for protecting the vulnerable communities. Remote sensing (RS) has the unique capability of earth observation with applications in global disaster response. Recent advances in micro-satellite constellations are offering opportunities for daily satellite image acquisition with a high spatial resolution. Such a high temporal resolution (i.e., daily) is the key to the success of RS for near realtime global flood monitoring. Moreover, the large volume of RS imagery further unleashes the vast potential of data-driven machine learning (ML) approaches for accurate flood mapping. Recent developments in deep learning, a sub-field of ML, have demonstrated the superiority of ML in flood mapping given massive RS data and corresponding labels. Unfortunately, training labels are very scarce in the domain of disaster response. This dissertation explores ML models for flood mapping without massive training labels by introducing the prior knowledge in RS data including the spatial, temporal, and spectral information: First, this dissertation proposes the PSNet to model flood mapping as bitemporal change detection by leveraging the temporal information of RS data. With only a few hand labels, the model can map floods with an over 0.85 F1 score for various heterogeneous flooded areas. Second, to further accelerate flood mapping, a self-supervised bitemporal change detection model, Patch-CVA, is developed to detect changed areas without hand labels. Further post-processing removes most false alarms by introducing the spatial and spectral information of floodwaters. Third, while the Patch-CVA has achieved good performance, its underlying assumption (i.e., most changes are flooded areas) may not hold in certain environments (e.g., crop growth). This dissertation proposes a weak label generation framework by fusing the spatial, temporal, and spectral information of geospatial data. A large weakly labeled dataset is obtained to train data-driven ML models for pixel-wise flood mapping with an over 0.9 F1 score.
Author: Christoph Aubrecht
Publisher:
ISBN: 9781013269363
Category : Science
Languages : en
Pages : 326
Get Book Here
Book Description
Over the past decades, rapid developments in digital and sensing technologies, such as the Cloud, Web and Internet of Things, have dramatically changed the way we live and work. The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites.This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
Author: Huan Wu
Publisher: John Wiley & Sons
ISBN: 1119427215
Category : Science
Languages : en
Pages : 352
Get Book Here
Book Description
Recent advances in the modeling and remote sensing of droughts and floods Droughts and floods are causing increasing damage worldwide, often with devastating short- and long-term impacts on human society. Forecasting when they will occur, monitoring them as they develop, and learning from the past to improve disaster management is vital. Global Drought and Flood: Observation, Modeling, and Prediction presents recent advances in the modeling and remote sensing of droughts and floods. It also describes the techniques and products currently available and how they are being used in practice. Volume highlights include: Remote sensing approaches for mapping droughts and floods Physical and statistical models for monitoring and forecasting hydrologic hazards Features of various drought and flood systems and products Use by governments, humanitarian, and development stakeholders in recent disaster cases Improving the collaboration between hazard information provision and end users The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals.
Author: Marco Helbich
Publisher: Springer
ISBN: 3319114697
Category : Political Science
Languages : en
Pages : 394
Get Book Here
Book Description
This book aims to promote the synergistic usage of advanced computational methodologies in close relationship to geospatial information across cities of different scales. A rich collection of chapters subsumes current research frontiers originating from disciplines such as geography, urban planning, computer science, statistics, geographic information science and remote sensing. The topics covered in the book are of interest to researchers, postgraduates, practitioners and professionals. The editors hope that the scientific outcome of this book will stimulate future urban-related international and interdisciplinary research, bringing us closer to the vision of a “new science of cities.”
Author: Fi-John Chang
Publisher: MDPI
ISBN: 3038975486
Category : Technology & Engineering
Languages : en
Pages : 376
Get Book Here
Book Description
Nowadays, the degree and scale of flood hazards has been massively increasing as a result of the changing climate, and large-scale floods jeopardize lives and properties, causing great economic losses, in the inundation-prone areas of the world. Early flood warning systems are promising countermeasures against flood hazards and losses. A collaborative assessment according to multiple disciplines, comprising hydrology, remote sensing, and meteorology, of the magnitude and impacts of flood hazards on inundation areas significantly contributes to model the integrity and precision of flood forecasting. Methodologically oriented countermeasures against flood hazards may involve the forecasting of reservoir inflows, river flows, tropical cyclone tracks, and flooding at different lead times and/or scales. Analyses of impacts, risks, uncertainty, resilience, and scenarios coupled with policy-oriented suggestions will give information for flood hazard mitigation. Emerging advances in computing technologies coupled with big-data mining have boosted data-driven applications, among which Machine Learning technology, with its flexibility and scalability in pattern extraction, has modernized not only scientific thinking but also predictive applications. This book explores recent Machine Learning advances on flood forecast and management in a timely manner and presents interdisciplinary approaches to modelling the complexity of flood hazards-related issues, with contributions to integrative solutions from a local, regional or global perspective.
Author: Guy J-P. Schumann
Publisher: John Wiley & Sons
ISBN: 1119217865
Category : Science
Languages : en
Pages : 266
Get Book Here
Book Description
Global Flood Hazard Subject Category Winner, PROSE Awards 2019, Earth Science Selected from more than 500 entries, demonstrating exceptional scholarship and making a significant contribution to the field of study. Flooding is a costly natural disaster in terms of damage to land, property and infrastructure. This volume describes the latest tools and technologies for modeling, mapping, and predicting large-scale flood risk. It also presents readers with a range of remote sensing data sets successfully used for predicting and mapping floods at different scales. These resources can enable policymakers, public planners, and developers to plan for, and respond to, flooding with greater accuracy and effectiveness. Describes the latest large-scale modeling approaches, including hydrological models, 2-D flood inundation models, and global flood forecasting models Showcases new tools and technologies such as Aqueduct, a new web-based tool used for global assessment and projection of future flood risk under climate change scenarios Features case studies describing best-practice uses of modeling techniques, tools, and technologies Global Flood Hazard is an indispensable resource for researchers, consultants, practitioners, and policy makers dealing with flood risk, flood disaster response, flood management, and flood mitigation.
Author: Zhenlong Li
Publisher: Routledge
ISBN: 1000261530
Category : Social Science
Languages : en
Pages : 233
Get Book Here
Book Description
Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically, analysed within this book are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems. Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, and unreliable in some aspects. It comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This book was originally published as a special issue of the International Journal of Digital Earth.
Author: Jiri Marsalek
Publisher: Springer Science & Business Media
ISBN: 1402049021
Category : Science
Languages : en
Pages : 349
Get Book Here
Book Description
These edited proceedings of the NATO Advanced Research Workshop detail the ongoing search for better ways of protecting human life, land, property and the environment by improved flood management. Such flood management is difficult enough in river basins controlled by a single authority, and becomes even more challenging when dealing with transboundary floods, which may originate in one country or jurisdiction and propagate downstream to another country, or jurisdiction.
Author: Alberto Refice
Publisher: Springer
ISBN: 3319639595
Category : Technology & Engineering
Languages : en
Pages : 213
Get Book Here
Book Description
This book is an overview of current state of the art about monitoring of inundation events through remote sensing. A complete approach to efficient and precise flood monitoring requires multiple fields of expertise, from image processing to hydrologic monitoring. This volume details the latest remote sensing techniques for flood monitoring and mapping, including use of optical data from geostationary sensors and LEO spacecraft, synthetic aperture radar (SAR) data analysis, and data fusion. Detailed case studies from a variety of subject experts illustrate these tools and techniques. Accurate monitoring of flood events is increasingly necessary to gain insight about both causes and remedies. Floods are one of the most destructive hazards to the human populations, they can occur practically everywhere on the Earth surface, and each year cause considerable harm and damage to infrastructures. The recent Flood directive in European Countries is contributing to a more quantitative approach to flood hazard and risk evaluation.
Author: Chunxiang Cao
Publisher: Springer Nature
ISBN: 9811582025
Category : Technology & Engineering
Languages : en
Pages : 153
Get Book Here
Book Description
This book introduces flood inundation area and flood risks assessment based on a comprehensive monitoring system using remote sensing and geographic information system technologies. Taking the 2011 flood disaster of Ayutthaya in Thailand as an example, it presents a flood intrusion zone identification method based on remote sensing technology, spatial information technology and geographic information system for flood disaster monitoring and early warning system. It introduces the study area and data, vegetation index, improved support vector machine and flood intrusion zone identification method. It also analyzes the flood remote sensing parameters and waterborne diseases, method of risk assessment of waterborne disease outbreak, waterborne disease outbreak risk monitoring based on backpropagation neural network and its expert system. It not only promotes a new interdisciplinary approach both in public health and space information technology, but also greatly supports decision makers in disaster reduction.
