Laser Scanning Applications In Landslide Assessment

This book is related to various applications of laser scanning in landslide assessment. Landslide detection approaches, susceptibility, hazard, vulnerability assessment and various modeling techniques are presented. Optimization of landslide conditioning parameters and use of heuristic, statistical, data mining approaches, their advantages and their relationship with landslide risk assessment are discussed in detail. The book contains scanning data in tropical forests; its indicators, assessment, modeling and implementation. Additionally, debris flow modeling and analysis including source of debris flow identification and rockfall hazard assessment are also presented.

Modern Technologies for Landslide Investigation and Prediction presents eleven contributed chapters from Chinese and Italian authors, as a follow-up of a bilateral workshop held in Shanghai on September 2013. Chapters are organized in three main parts: ground-based monitoring techniques (photogrammetry, terrestrial laser scanning, ground-based InSAR, infrared thermography, and GNSS networks), geophysical (passive seismic sensor networks) and geotechnical methods (SPH and SLIDE), and satellite remote-sensing techniques (InSAR and optical images). Authors of these contributes are internationally-recognized experts in their respective research fields. Marco Scaioni works in the college of Surveying and Geo-Informatics at Tongji University, Shanghai (P.R. China). His research fields are mainly Close-range Photogrammetry, Terrestrial Laser Scanning, and other ground-based sensors for metrological and deformation monitoring applications to structural engineering and geosciences. In the period 2012-2016 he is chairman of the Working Group V/3 in the International Society for Photogrammetry and Remote Sensing, focusing on ‘Terrestrial 3D Imaging and Sensors’.

This book gathers the proceedings of the 1st Global Civil Engineering Conference, GCEC 2017, held in Kuala Lumpur, Malaysia, on July 25–28, 2017. It highlights how state-of-the-art techniques and tools in various disciplines of Civil Engineering are being applied to solve real-world problems. The book presents interdisciplinary research, experimental and/or theoretical studies yielding new insights that will advance civil engineering methods. The scope of the book spans the following areas: Structural, Water Resources, Geotechnical, Construction, Transportation Engineering and Geospatial Engineering applications.

Over the years, the interactions between land, ocean, biosphere and atmosphere have increased, mainly due to population growth and anthropogenic activities, which have impacted the climate and weather conditions at local, regional and global scales. Thus, natural hazards related to climate changes have significantly impacted human life and health on different spatio-temporal scales and with socioeconomic bearings. To monitor and analyze natural hazards, satellite data have been widely used in recent years by many developed and developing countries. In an effort to better understand and characterize the various underlying processes influencing natural hazards, and to carry out related impact assessments, Natural Hazards: Earthquakes, Volcanoes, and Landslides, presents a synthesis of what leading scientists and other professionals know about the impacts and the challenges when coping with climate change. Combining reviews of theories and methods with analysis of case studies, the book gives readers research information and analyses on satellite geophysical data, radar imaging and integrated approaches. It focuses also on dust storms, coastal subsidence and remote sensing mapping. Some case studies explore the roles of remote sensing related to landslides and volcanoes. Overall, improved understanding of the processes leading to these hazardous events will help scientists predict their occurrence. Features Provides information on the physics and physical processes of natural hazards, their monitoring and the mapping of damages associated with these hazards Explains how natural hazards are strongly associated with coupling between land–ocean–atmosphere Includes a comprehensive overview of the role of remote sensing in natural hazards worldwide Examines risk assessment in urban areas through numerical modelling and geoinformation technologies Demonstrates how data analysis can be used to aid in prediction and management of natural hazards

This book aims to promote the core understanding of a proper modelling of road traffic accidents by deep learning methods using traffic information and road geometry delineated from laser scanning data. The first two chapters of the book introduce the reader to laser scanning technology with creative explanation and graphical illustrations, review and recent methods of extracting geometric road parameters. The next three chapters present different machine learning and statistical techniques applied to extract road geometry information from laser scanning data. Chapters 6 and 7 present methods for modelling roadside features and automatic road geometry identification in vector data. After that, this book goes on reviewing methods used for road traffic accident modelling including accident frequency and injury severity of the traffic accident (Chapter 8). Then, the next chapter explores the details of neural networks and their performance in predicting the traffic accidents along with a comparison with common data mining models. Chapter 10 presents a novel hybrid model combining extreme gradient boosting and deep neural networks for predicting injury severity of road traffic accidents. This chapter is followed by deep learning applications in modelling accident data using feed-forward, convolutional, recurrent neural network models (Chapter 11). The final chapter (Chapter 12) presents a procedure for modelling traffic accident with little data based on the concept of transfer learning. This book aims to help graduate students, professionals, decision makers, and road planners in developing better traffic accident prediction models using advanced neural networks.

This book focuses on different algorithms and models related to AI, big data and IoT used for various domains. It enables the reader to have a broader and deeper understanding of several perspectives regarding the dynamics, challenges, and opportunities for sustainable development using artificial intelligence, big data and IoT. Applications of Artificial Intelligence, Big Data and Internet of Things (IoT) in Sustainable Development focuses on IT-based advancements in multidisciplinary fields such as healthcare, finance, bioinformatics, industrial automation, and environmental science. The authors discuss the key issues of security, management, and the realization of possible solutions to hurdles in sustainable development. The reader will master basic concepts and deep insights of various algorithms and models for various applications such as healthcare, finance, education, smart cities, smart cars, among others. Finally, the book will also examine the applications and implementation of big data IoT, AI strategies to facilitate the sustainable development goals set by the United Nations by 2030. This book is intended to help researchers, academics, and policymakers to analyze the challenges and future aspects for maintaining sustainable development through IoT, big data, and AI.

This book is a printed edition of the Special Issue "Application of Artificial Neural Networks in Geoinformatics" that was published in Applied Sciences