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Mapping of the risk of coastal erosion for two case studies: Pianosa island (Tuscany) and Piscinas (Sardinia) (Book chapter)

Book title: Eighth International Symposium “Monitoring of Mediterranean Coastal Areas. Problems and Measurement Techniques”

Authors: --- --- --- --- et al.
Book Series: Proceedings e report ISSN: 27045846 ISBN: 9788855181471 Year: Volume: 126 DOI: 10.36253/978-88-5518-147-1.71
Publisher: Firenze University Press
Subject: Forestry
Added to DOAB on : 2020-11-26 11:35:25
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Abstract

This study focuses on the use of remote sensing to generate coastal erosion risk maps for Pianosa Island (Tuscany) and Piscinas dune system (Sardinia). The method made use of both ancillary and satellite data (Sentinel-2), in addition to SAR images (COSMO SkyMed and Sentinel-1B). TOA radiance products were atmospherically corrected and processed using Sen2Coral and BOMBER in order to map different marine substrates and bathymetry. The coastal erosion risk maps have been generated based on these output and the results confirm that the coasts of these sites don’t have coastal erosion problems.

Remote Sensing based Building Extraction

Authors: --- --- ---
ISBN: 9783039283828 9783039283835 Year: Pages: 442 DOI: 10.3390/books978-3-03928-383-5 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Technology (General) --- General and Civil Engineering --- Construction
Added to DOAB on : 2020-04-07 23:07:09
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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

Keywords

roof segmentation --- outline extraction --- convolutional neural network --- boundary regulated network --- very high resolution imagery --- building boundary extraction --- convolutional neural network --- active contour model --- high resolution optical images --- LiDAR --- richer convolution features --- building edges detection --- high spatial resolution remote sensing imagery --- building --- modelling --- reconstruction --- change detection --- LiDAR --- point cloud --- 3-D --- building extraction --- deep learning --- attention mechanism --- very high resolution --- imagery --- building detection --- aerial images --- feature-level-fusion --- straight-line segment matching --- occlusion --- building regularization technique --- point clouds --- boundary extraction --- regularization --- building reconstruction --- digital building height --- 3D urban expansion --- land-use --- DTM extraction --- open data --- developing city --- accuracy analysis --- building detection --- building index --- feature extraction --- mathematical morphology --- morphological attribute filter --- morphological profile --- building extraction --- deep learning --- semantic segmentation --- data fusion --- high-resolution satellite images --- GIS data --- high-resolution aerial images --- deep learning --- generative adversarial network --- semantic segmentation --- Inria aerial image labeling dataset --- Massachusetts buildings dataset --- building extraction --- simple linear iterative clustering (SLIC) --- multiscale Siamese convolutional networks (MSCNs) --- binary decision network --- unmanned aerial vehicle (UAV) --- image fusion --- high spatial resolution remotely sensed imagery --- object recognition --- deep learning --- method comparison --- LiDAR point cloud --- building extraction --- elevation map --- Gabor filter --- feature fusion --- semantic segmentation --- urban building extraction --- deep convolutional neural network --- VHR remote sensing imagery --- U-Net --- remote sensing --- deep learning --- building extraction --- web-net --- ultra-hierarchical sampling --- 3D reconstruction --- indoor modelling --- mobile laser scanning --- point clouds --- 5G signal simulation --- building extraction --- high-resolution aerial imagery --- fully convolutional network --- semantic segmentation --- n/a

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