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Geospatial intelligenceapplications and future trends /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Geospatial intelligenceedited by Fatimazahra Barramou ... [et al.].
其他題名:	applications and future trends /
其他作者:	Barramou, Fatimazahra.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	x, 180 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Geospatial dataComputer processing.
電子資源:	https://doi.org/10.1007/978-3-030-80458-9
ISBN:	9783030804589$q(electronic bk.)

Geospatial intelligenceapplications and future trends /
Geospatial intelligenceapplications and future trends /[electronic resource] :edited by Fatimazahra Barramou ... [et al.]. - Cham :Springer International Publishing :2022. - x, 180 p. :ill. (some col.), digital ;24 cm. - Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development,2522-8714. - Advances in science, technology & innovation, IEREK interdisciplinary series for sustainable development..

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.

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.

ISBN: 9783030804589$q(electronic bk.)

Standard No.: 10.1007/978-3-030-80458-9doiSubjects--Topical Terms:

862765
Geospatial data
--Computer processing.

LC Class. No.: G70.217.G46 / G46 2022

Dewey Class. No.: 910.285

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505 0 $a 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.
520 $a 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.
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