Applications Of Remote Sensing And Gis Based On An Innovative Vision

This book covers various aspects of remote sensing and geographic information systems, from the perspective of earth and environmental sciences. The theme of applications of remote sensing and geographic information systems for the purposes of sustainable development highlights the innovative usage of space imaged spectral data in soil characterization. This book merges the selected contributions to the First International Conference of Remote Sensing and Space Sciences Applications (Egypt 2022) aiming to promote the latest findings on the development of Space Technologies and Applications.

Remote sensing of our environment is becoming increasingly accessible and important in today’s society. This book aims to highlight some of the broad and multi-disciplinary applications, and emerging practices, that remote sensing and photogrammetric technologies lend themselves to. The papers have been selected from the 13th and 14th Australasian Remote Sensing and Photogrammetry Conferences given by experts in remote sensing, spatial analysis and photogrammetry from across the Asia Pacific region. They are presented here as a collection of peer reviewed papers covering research into areas such as data fusion techniques and their applications in environmental monitoring, synoptic monitoring and data processing, terrestrial and marine applications of remote sensing, and photogrammetry.

This book thoroughly covers the remote sensing visualization and analysis techniques based on computational imaging and vision in Earth science. Remote sensing is considered a significant information source for monitoring and mapping natural and man-made land through the development of sensor resolutions that committed different Earth observation platforms. The book includes related topics for the different systems, models, and approaches used in the visualization of remote sensing images. It offers flexible and sophisticated solutions for removing uncertainty from the satellite data. It introduces real time big data analytics to derive intelligence systems in enterprise earth science applications. Furthermore, the book integrates statistical concepts with computer-based geographic information systems (GIS). It focuses on image processing techniques for observing data together with uncertainty information raised by spectral, spatial, and positional accuracy of GPS data. The book addresses several advanced improvement models to guide the engineers in developing different remote sensing visualization and analysis schemes. Highlights on the advanced improvement models of the supervised/unsupervised classification algorithms, support vector machines, artificial neural networks, fuzzy logic, decision-making algorithms, and Time Series Model and Forecasting are addressed. This book guides engineers, designers, and researchers to exploit the intrinsic design remote sensing systems. The book gathers remarkable material from an international experts' panel to guide the readers during the development of earth big data analytics and their challenges.

Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.

This two-volume set LNCS 11196 and LNCS 11197 constitutes the refereed proceedings of the 7th International Conference on Digital Heritage, EuroMed 2018, held in Nicosia, Cyprus, in October/November 2018. The 21 full papers, 47 project papers, and 29 short papers presented were carefully reviewed and selected from 537 submissions. The papers are organized in topical sections on 3D Digitalization, Reconstruction, Modeling, and HBIM; Innovative Technologies in Digital Cultural Heritage; Digital Cultural Heritage –Smart Technologies; The New Era of Museums and Exhibitions; Digital Cultural Heritage Infrastructure; Non Destructive Techniques in Cultural Heritage Conservation; E-Humanities; Reconstructing the Past; Visualization, VR and AR Methods and Applications; Digital Applications for Materials Preservation in Cultural Heritage; and Digital Cultural Heritage Learning and Experiences.

This book focuses on monitoring and assessing various environmental processes in Tunisia using state-of-the-art remote sensing and GIS technologies. In addition to addressing the diversity of Tunisian landscapes and providing spatial analysis of natural, cultivated and urbanized environments. It presents and discusses several case studies on integrated RS / GIS approaches for mapping, modeling, monitoring and evaluation. Moreover, in this volume authored by experts in the topic from Tunisia and other countries, authors assess the agro-environmental applications from Tunisia and offer different methods and applications to environmental processes and risks including drought, degradation, flood, planning, Yield estimation, dust storm detection, dry land vulnerability, wetland dynamics and others. The material presented here will help decision-makers plan sustainable landscape and agricultural management policies that preserve biodiversity and contribute to achieving sustainability goals and for researchers, it will expose methodological approaches used in different fields of research. Graduate students and Practionioner engineers working in the field of RS/GIS will also benefit from the book. The book ends with a set of conclusions and recommendations to support researchers underscoring the need for further research in this area.

How to use remote sensing technology as geographic data is demonstrated, as is how remote sensing products are the perfect complement to GIS-based analysis in industries such as emergency response, meteorology, water resources, land use and urban planning.

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).