Remote Sensing Based Building Extraction


Book Description

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D




Remote Sensing based Building Extraction


Book Description

Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D.




Remote Sensing Based Building Extraction II.


Book Description

Building extraction from remote sensing data plays an important role in geospatial applications such as urban planning, disaster management, navigation, and updating geographic databases. The rapid development of image processing techniques and the accessibility of very-high-resolution multispectral, hyperspectral, LiDAR, and SAR remote sensing images have further boosted research on building-extraction-related topics. In particular, to meet the recent demand for advanced artificial intelligence models, many research institutes and associations have provided open source datasets and annotated training data, presenting new opportunities to develop advanced approaches for building extraction and monitoring. Hence, there are higher expectations of the efficiency, accuracy, and robustness of building extraction approaches. Additionally, they should meet the demand for processing large city-, national-, and global-scale datasets. Moreover, learning and dealing with imperfect training data remains a challenge, as does unexpected objects in urban scenes such as trees, clouds, and shadows. In addition to building masks, more research has arisen on the automatic generation of LoD2/3 building models from remote sensing data. This follow-up Special Issue of "Remote Sensing-based Building Extraction", has collected more research on cutting-edge approaches to essential urban processes such as 3D reconstruction, automatic building segmentation, and 3D roof modelling.




Using Remote-sensing and Gis Technology for Automated Building Extraction


Book Description

Extraction of buildings from remote sensing sources is an important GIS application and has been the subject of extensive research over the last three decades. An accurate building inventory is required for applications such as GIS database maintenance and revision; impervious surfaces mapping; storm water management; hazard mitigation and risk assessment. Despite all the progress within the fields of photogrammetry and image processing, the problem of automated feature extraction is still unresolved. : A methodology for automatic building extraction that integrates remote sensing sources and GIS data was proposed. The methodology consists of a series of image processing and spatial analysis techniques. It incorporates initial simplification procedure and multiple feature analysis components. The extraction process was implemented and tested on three distinct types of buildings including commercial, residential and high-rise. Aerial imagery and GIS data from Shelby County, Tennessee were identified for the testing and validation of the results. The contribution of each component to the overall methodology was quantitatively evaluated as relates to each type of building. The automatic process was compared to manual building extraction and provided means to alleviate the manual procedure effort.




Automatic Extraction of Man-Made Objects from Aerial and Space Images (II)


Book Description

Advancements in digital sensor technology, digital image analysis techniques, as well as computer software and hardware have brought together the fields of computer vision and photogrammetry, which are now converging towards sharing, to a great extent, objectives and algorithms. The potential for mutual benefits by the close collaboration and interaction of these two disciplines is great, as photogrammetric know-how can be aided by the most recent image analysis developments in computer vision, while modern quantitative photogrammetric approaches can support computer vision activities. Devising methodologies for automating the extraction of man-made objects (e.g. buildings, roads) from digital aerial or satellite imagery is an application where this cooperation and mutual support is already reaping benefits. The valuable spatial information collected using these interdisciplinary techniques is of improved qualitative and quantitative accuracy. This book offers a comprehensive selection of high-quality and in-depth contributions from world-wide leading research institutions, treating theoretical as well as implementational issues, and representing the state-of-the-art on this subject among the photogrammetric and computer vision communities.




Advanced Technologies for Humanity


Book Description

This book gathers the proceedings of the International Conference on Advanced Technologies for Humanity (ICATH’2021), held on November 26-27, 2021, in INSEA, Rabat, Morocco. ICATH’2021 was jointly co-organized by the National Institute of Statistics and Applied Economics (INSEA) in collaboration with the Moroccan School of Engineering Sciences (EMSI), the Hassan II Institute of Agronomy and Veterinary Medicine (IAV-Hassan II), the National Institute of Posts and Telecommunications (INPT), the National School of Mineral Industry (ENSMR), the Faculty of Sciences of Rabat (UM5-FSR), the National School of Applied Sciences of Kenitra (ENSAK) and the Future University in Egypt (FUE). ICATH’2021 was devoted to practical models and industrial applications related to advanced technologies for Humanity. It was considered as a meeting point for researchers and practitioners to enable the implementation of advanced information technologies into various industries. This book is helpful for PhD students as well as researchers. The 48 full papers were carefully reviewed and selected from 105 submissions. The papers presented in the volume are organized in topical sections on synergies between (i) smart and sustainable cities, (ii) communication systems, signal and image processing for humanity, (iii) cybersecurity, database and language processing for human applications, (iV) renewable and sustainable energies, (V) civil engineering and structures for sustainable constructions, (Vi) materials and smart buildings and (Vii) Industry 4.0 for smart factories. All contributions were subject to a double-blind review. The review process was highly competitive. We had to review 105 submissions from 12 countries. A team of over 100 program committee members and reviewers did this terrific job. Our special thanks go to all of them.




Object-Based Image Analysis


Book Description

This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).




Fundamentals of Deep Learning


Book Description

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. Companies such as Google, Microsoft, and Facebook are actively growing in-house deep-learning teams. For the rest of us, however, deep learning is still a pretty complex and difficult subject to grasp. If you’re familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started. Examine the foundations of machine learning and neural networks Learn how to train feed-forward neural networks Use TensorFlow to implement your first neural network Manage problems that arise as you begin to make networks deeper Build neural networks that analyze complex images Perform effective dimensionality reduction using autoencoders Dive deep into sequence analysis to examine language Learn the fundamentals of reinforcement learning




Remote Sensing of Impervious Surfaces


Book Description

Remote sensing of impervious surfaces has matured using advances in geospatial technology so recent that its applications have received only sporadic coverage in remote sensing literature. Remote Sensing of Impervious Surfaces is the first to focus entirely on this developing field. It provides detailed coverage of mapping, data extraction,




Advances in Remote Sensing for Infrastructure Monitoring


Book Description

This volume provides international case studies of practical and advanced methods using satellite images integrated with other airborne, drone images and field data to monitor infrastructure. The book is timely, as infrastructure spending by national governments is increasing and robust monitoring techniques are needed to keep pace with climate change impacts affecting infrastructures globally. The expert international contributions that comprise the book provide examples of advanced methods using InSAR, high-resolution optical and radar images, LIDAR, UAV, geophysical techniques and their applications to civil infrastructure. The case studies focus on high-resolution, rapid time-series radar interferometry to monitor highways, railways, pipelines, bridges, urban, and water conveyance infrastructures. Other case studies use optical and radar images to characterize urban infrastructure and monitor damages from floods, oil spills and conflicts. The case studies are global focusing on infrastructure projects in Canada, Dominica Guyana, India Italy, Syria Taiwan, United States and the United Kingdom. This compilation of selected case studies will provide useful guidelines for the civil infrastructure characterization and monitoring communities. The book will be of interest to infrastructure consultants and professionals, scientific communities in earth observation and advanced imaging methods, and researchers and professors in earth sciences, climate change, and civil and geoengineering.