Hui Lin – författare

Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas offers a complete and thorough system for using optical and synthetic aperture radar (SAR) remote sensing data for improving impervious surface estimation (ISE). Highlighting tropical and subtropical areas where there is significant cloud occurrence and varying phenology, the book addresses the challenges impacting impervious surfaces in tropical and subtropical zones. It examines the potential for estimating urban impervious surfaces in a rainy and cloudy environment, considers the difficulties encountered when using optical remote sensing in this type of climate, and assesses existing methods employing remote sensing data for accurate ISE in tropical and subtropical regions. Using the results of comparative studies conducted during the four seasons and in six different cities (Guangzhou, Shenzhen, Hong Kong, Mumbai, Sao Paulo, and Cape Town), the authors develop a framework for ISE using optical and SAR image data. They address the advantages and disadvantages of optical and SAR data, consider fusion strategies for combining optical and SAR data, and examine different feature extractions for optical and SAR data. They also detail the limitations of the research, suggest possible topics for future analysis, and cover previous findings on the synergistic use of optical and SAR data. Concentrates on the effect a tropical and subtropical urban climate can have on impervious surface estimation (ISE)Reviews literature on the significance of ISE and the phonological and climatic characteristics of tropical and subtropical regionsDescribes datasets including satellite data, digital orthophoto data, in situ data, and more Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas investigates the state of the art in creating new algorithms for digital imag

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes.Key Features:• It covers both technical and soft skills.• It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment.• It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes.Key Features:• It covers both technical and soft skills.• It has a chapter dedicated to the big data cloud environment. For industry applications, the practice of data science is often in such an environment.• It is hands-on. We provide the data and repeatable R and Python code in notebooks. Readers can repeat the analysis in the book using the data and code provided. We also suggest that readers modify the notebook to perform analyses with their data and problems, if possible. The best way to learn data science is to do it!

Guidance for security-oriented management of operational technology and insights on new cyber-physical security solutions for smart grids and renewable energy Cyber-Physical Security and Resilience for Smart Grids and Renewable Energy describes state-of-the-art technologies of cyber security approaches to increase the resilience of cyber-physical infrastructures used by power grids. It begins by presenting fundamental knowledge of cyber security, computer networks, and current physical processes of power systems with the integration of distributed energy resources. By studying the forensic analysis of representative security incidents in power grids, the author explains the fundamental construction and emerging challenges unique to today’s power grids. Cyber-Physical Security and Resilience for Smart Grids and Renewable Energy includes information on: Security methods retrofitted according to domain-specific knowledge of power systems and distributed energy resourcesPossible solutions from the perspectives of systems specification, system modeling, network programming, and formal verificationReal attack incidents and outage accidents caused by cyber-physical attacks and disruption against smart gridsCyber-Physical Security and Resilience for Smart Grids and Renewable Energy is an excellent resource for students and engineers seeking guidance for security-oriented management of operational technology. It also serves as a starting point for academic researchers exploring new cyber-physical security solutions.

Remote Sensing of Impervious Surfaces in Tropical and Subtropical Areas offers a complete and thorough system for using optical and synthetic aperture radar (SAR) remote sensing data for improving impervious surface estimation (ISE). Highlighting tropical and subtropical areas where there is significant cloud occurrence and varying phenology, the book addresses the challenges impacting impervious surfaces in tropical and subtropical zones. It examines the potential for estimating urban impervious surfaces in a rainy and cloudy environment, considers the difficulties encountered when using optical remote sensing in this type of climate, and assesses existing methods employing remote sensing data for accurate ISE in tropical and subtropical regions. Using the results of comparative studies conducted during the four seasons and in six different cities (Guangzhou, Shenzhen, Hong Kong, Mumbai, Sao Paulo, and Cape Town), the authors develop a framework for ISE using optical and SAR image data. They address the advantages and disadvantages of optical and SAR data, consider fusion strategies for combining optical and SAR data, and examine different feature extractions for optical and SAR data. They also detail the limitations of the research, suggest possible topics for future analysis, and cover previous findings on the synergistic use of optical and SAR data. Concentrates on the effect a tropical and subtropical urban climate can have on impervious surface estimation (ISE)Reviews literature on the significance of ISE and the phonological and climatic characteristics of tropical and subtropical regionsDescribes datasets including satellite data, digital orthophoto data, in situ data, and moreRemote Sensing of Impervious Surfaces in Tropical and Subtropical Areas investigates the state of the art in creating new algorithms for digital images processing and remotely sensed images classification, as well as in developing the meteorological modeling of urban heat islands, and the hydrological modeling of surface run-off and urban floods.

This book describes how powerful computing technology, emerging big and open data sources, and theoretical perspectives on spatial synthesis have revolutionized the way in which we investigate social sciences and humanities.

This book describes how powerful computing technology, emerging big and open data sources, and theoretical perspectives on spatial synthesis have revolutionized the way in which we investigate social sciences and humanities.

This book constitutes the proceedings of the Second International Conference on Spatial Data and Intelligence, SpatialDI 2021, which was held during April 22-24, 2021 in Hangzhou, China.The 14 full papers and 7 short papers presented in this volume were carefully reviewed and selected from 72 submissions.

This book is a collection of seminal position essays by leading researchers on new development in Geographic Information Sciences (GIScience), covering a wide range of topics and representing a variety of perspectives.