Geospatial Intelligence

Applications and Future Trends
Herausgegeben:BARRAMOU, Fatimazahra; El BRIRCHI, El Hassan; MANSOURI, Khalifa; DEHBI, Youness

• Broschiertes Buch

Produktbeschreibung

This book explores cutting-edge methods combining geospatial technologies and artificial intelligence related to several fields such as smart farming, urban planning, geology, transportation, and 3D city models. It introduces techniques which range from machine and deep learning to remote sensing for geospatial data analysis.

The book consists of two main parts that include 13 chapters contributed by promising authors. The first part deals with the use of artificial intelligence techniques to improve spatial data analysis, whereas the second part focuses on the use of artificial intelligence with remote sensing in various fields. Throughout the chapters, the interest for the use of artificial intelligence is demonstrated for different geospatial technologies such as aerial imagery, drones, Lidar, satellite remote sensing, and more.

The work in this book is dedicated to the scientific community interested in the coupling of geospatial technologies and artificial intelligence and exploring the synergetic effects of both fields. It offers practitioners and researchers from academia, the industry and government information, experiences and research results about all aspects of specialized and interdisciplinary fields on geospatial intelligence.

Produktdetails

• Advances in Science, Technology & Innovation
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-80460-2
• 1st ed. 2022
• Seitenzahl: 192
• Erscheinungstermin: 12. November 2022
• Englisch
• Abmessung: 279mm x 210mm x 11mm
• Gewicht: 483g
• ISBN-13: 9783030804602
• ISBN-10: 3030804607
• Artikelnr.: 66104434

Autorenporträt

Prof. Fatimazahra BARRAMOU: Fatimazahra BARRAMOU is Professor of Geoinformatics and Researcher in the Department of Mathematics, Informatics and Geomatics at Hassania Engineering School of Public Works (EHTP), Casablanca, Morocco. In 2009, she earned an Engineering Degree in Geographic Information Sciences from EHTP. She received her Ph.D. in computer sciences from Hassan II University of Casablanca in 2015. She is also Member of the Geomatics Science Research Team "SGEO". Her current research activities focus on geomatics, geo-computational intelligence, and geosciences. Professor Barramou is General Chair of the International Conference MORGEO 2020. She was Member of program committees of several conferences in the field of geomatics and geospatial technologies.  Prof El Hassan El BRIRCHI: ELHassan ELBrirchi is Associate Professor of Geodesy and Surveying and Researcher at Hassania Engineering School of Public Works (EHTP), Casablanca, Morocco. He is Head of EHTP Geographic Information Sciences Engineering Program and Co-founding Member of the Moroccan Association of Geomatics (AMAGEO). As Member of the Geomatics Science Research Team "SGEO", his research area covers the fields of physical geodesy, transportation planning, and GIS-T analysis. He also participates to scientific program committees for several research conferences. He earned his Ph.D. in Physical Geodesy from Science and Technology University (FST) of Fès in 2013. Professor ELBrirchi earned his Geography Engineering degree from ENSG-IGN France in 2003 and his Engineering Degree in Geographic Information Sciences from EHTP in 2001.   Prof Khalifa MANSOURI: Khalifa Mansouri is Professor of Computer Science and Researcher at the Hassan II University, Casablanca, Morocco. He is Head of the research team "Distributed IT systems" and Deputy Director of educational affairs at ENSET Institute in Mohammédia, Morocco. His researchis focused on real-time systems, information systems, e-learning systems, and industrial systems (modeling, optimization, and numerical computing). He earned an ENSET diploma from Mohammedia in 1991, Ph.D. degree (calculation and optimization of structures) in 1994, HDR in 2010, and Ph.D. (computer science) in 2016 from Hassan II University in Casablanca.   Dr.-Ing. Youness DEHBI:  Youness Dehbi studied computer science and communication research at the University of Bonn in Germany. After graduating in 2008, he started his career as Research Assistant in the geoinformation group at the Institute of Geodesy and Geoinformation at the University of Bonn, where he is currently teaching and researching in the field of machine learning for 3D city models. Having completed his Ph.D., he is currently responsible for the urban reasoning and analytics field as Senior Researcher. His research focus is both the efficient semantic interpretation of urban environments from dense observations and the probabilistic estimation of 3D city models from sparse noisy data. Dehbi has published a range of scientific research papers including awarded papers. He is also acting as Reviewer for several research journals and Member of various scientific committees.  

Inhaltsangabe

Spatial Data and Artificial Intelligence.- Towards a Multi-agents Model for Automatic Big Data Processing to Support Urban planning.- Geospatial Forecasting and Social Media Exploration Based on Sentiment Analysis: Application to Flood Forecasting.- Deep Convolution Neural Network for Automated Method of Road Extraction on Aerial Imagery.- Enhancing the Management of Traffic Sequence Following Departure Trajectories.- A Multiagent and Machine Learning based Intrusion Detection System for Drone Networks.- Towards a Deep Learning Approach for Automatic Semantic Segmentation of 3D Lidar point Clouds in Urban Areas.- Artificial and Geospatial Intelligence Driven Digital Twins Architecture Development Against the Worldwide Twin Crisis Caused by Covid-19.- Remote Sensing and Artificial Intelligence.- Opportunities for Artificial Intelligence in Precision Agriculture Using Satellite remote Sensing.- Monitoring Land Productivity Trends in Souss-Massa Region Using Landsat Time series Data to Support SDG Target 15.3.- Subimages Based Approach for Landslide Susceptibility Mapping Using Convolutional Neural Network.- Lithological Mapping for a Semi-arid Area Using GEOBIA and PBIA Machine Learning Approaches with Sentinel-2 Imagery: Case Study of Skhour Rehamna, Morocco.- Optimization of Object-based Image Analysis with Genetic Programming to Generate Explicit Knowledge from Worldview 2 Data for Urban Mapping.- Machine Learning and Remote Sensing in Mapping and Estimating Rosemary Cover biomass.