Centre de documentation
scientifique

Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images / Lingdong Mao in Landscape and Urban Planning, vol 222 (June 2022)  

Public [article]  inLandscape and Urban Planning > vol 222 (June 2022) . - n° 104384 Titre : Large-scale automatic identification of urban vacant land using semantic segmentation of high-resolution remote sensing images Type de document : Article/Communication Auteurs : Lingdong Mao, Auteur ; Zhe Zheng, Auteur ; Xiangfeng Meng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104384 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] détection d'objet [Termes IGN] grande échelle [Termes IGN] identification automatique [Termes IGN] image à haute résolution [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique Résumé : (auteur) Urban vacant land is a growing issue worldwide. However, most of the existing research on urban vacant land has focused on small-scale city areas, while few studies have focused on large-scale national areas. Large-scale identification of urban vacant land is hindered by the disadvantage of high cost and high variability when using the conventional manual identification method. Criteria inconsistency in cross-domain identification is also a major challenge. To address these problems, we propose a large-scale automatic identification framework of urban vacant land based on semantic segmentation of high-resolution remote sensing images and select 36 major cities in China as study areas. The framework utilizes deep learning techniques to realize automatic identification and introduces the city stratification method to address the challenge of identification criteria inconsistency. The results of the case study on 36 major Chinese cities indicate two major conclusions. First, the proposed framework of vacant land identification can achieve over 90 percent accuracy of the level of professional auditors with much higher result stability and approximately 15 times higher efficiency compared to the manual identification method. Second, the framework has strong robustness and can maintain high performance in various cities. With the above advantages, the proposed framework provides a practical approach to large-scale vacant land identification in various countries and regions worldwide, which is of great significance for the academic development of urban vacant land and future urban development. Numéro de notice : A2022-267 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.landurbplan.2022.104384 Date de publication en ligne : 03/03/2022 En ligne : https://doi.org/10.1016/j.landurbplan.2022.104384 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100275 [article]

Predicting AIS reception using tropospheric propagation forecast and machine learning / Zackary Vanche (2022)  

Public Titre : Predicting AIS reception using tropospheric propagation forecast and machine learning Type de document : Article/Communication Auteurs : Zackary Vanche, Auteur ; Ambroise Renaud, Auteur ; Aldo Napoli, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2022 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : ISAP 2022, IEEE AP-S/USNC-URSI International Symposium on Antennas & Propagation 10/07/2022 Denver Colorado - Etats-Unis Proceedings IEEE Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] identification automatique [Termes IGN] navigation maritime [Termes IGN] navire [Termes IGN] récepteur [Termes IGN] troposphère Résumé : (auteur) The aim of this paper is to present a methodology for modelling and predicting the coverage of an Automatic Identification System (AIS) station based on tropospheric index forecast maps and modelling methods from machine learning. The aim of this work is to cartographically represent the areas in which the AIS signals emitted by ships will be received by a coastal station. This work contributes to the improvement of maritime situational awareness and to the detection of anomalies at sea [1], and in particular to the identification of AIS message falsifications [2] (ubiquity of a vessel by identity theft, falsification of GPS positions and deactivation of AIS). Numéro de notice : C2022-036 Affiliation des auteurs : ENSG+Ext (2020- ) Autre URL associée : vers HAL Thématique : POSITIONNEMENT Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.23919/USNC-URSI52669.2022.9887465 En ligne : https://doi.org/10.23919/USNC-URSI52669.2022.9887465 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101606

Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)  

Public [article]  inGeodetski vestnik > vol 65 n° 2 (June - August 2021) . - pp 234 - 259 Titre : Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops Type de document : Article/Communication Auteurs : Nina Kranjec, Auteur ; Mihaela Triglav Cekada, Auteur ; Milan Kobal, Auteur Année de publication : 2021 Article en page(s) : pp 234 - 259 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Acer pseudoplatanus [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] Fagus sylvatica [Termes IGN] feuillu [Termes IGN] figure géométrique [Termes IGN] Fraxinus excelsior [Termes IGN] houppier [Termes IGN] identification automatique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Larix decidua [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] Picea abies [Termes IGN] Pinophyta [Termes IGN] Pinus sylvestris [Termes IGN] semis de points [Termes IGN] Slovénie Résumé : (auteur) Based on the laser point clouds of 240 individual trees that were also identified in the field, we developed decision trees to distinguish deciduous and coniferous trees and individual tree species: Picea abies, Larix decidua, Pinus sylvestris, Fagus sylvatica, Acer pseudoplatanus, Fraxinus excelsior. The volume of the upper part of the tree crown (height of 3 m) and the average intensity of the laser reflections were used as explanatory variables. There were four aerial laser datasets: May 2012, September 2012, March 2013 and July 2015. We found that the combination of the volume and the average intensity of the first three laser datasets was the most reliable for predicting the selected tree species (60% model performance). A slightly poorer model performance was obtained if only the average intensity of the first three datasets was used (54% model performance). The worst model performance was given by the intensities (31 % model performance) or the volumes (21 % model performance) of dataset 4, which represents the national laser scanning of Slovenia (LSS). The best performing was the deciduous and coniferous separation, which achieved 75% and 95% success based on the test data (combination of volume and average intensity of the first three laser datasets). Using only the LSS intensities, deciduous and coniferous trees could be separated with 81% success. Numéro de notice : A2021-559 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2021.02.234-259 Date de publication en ligne : 27/05/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.02.234-259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98113 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2021021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques / Tarun Kumar in Geocarto international, vol 32 n° 12 (December 2017)  

Public [article]  inGeocarto international > vol 32 n° 12 (December 2017) . - pp 1367 - 1388 Titre : Identification of rainwater harvesting sites using SCS-CN methodology, remote sensing and Geographical Information System techniques Type de document : Article/Communication Auteurs : Tarun Kumar, Auteur ; D. C. Jhariya, Auteur Année de publication : 2017 Article en page(s) : pp 1367 - 1388 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aquifère [Termes IGN] conservation des ressources naturelles [Termes IGN] eau pluviale [Termes IGN] eau souterraine [Termes IGN] identification automatique [Termes IGN] image satellite [Termes IGN] Inde [Termes IGN] ressources en eau [Termes IGN] ruissellement [Termes IGN] site [Termes IGN] système d'information géographique Résumé : (Auteur) This study presents a method to identify potential sites for soil and water conservation techniques for the demarcation of suitable sites for artificial recharge of groundwater aquifers, in the study area. The run-off derived by the Soil Conservation Service Curve Number method is a function of run-off potential which can be expressed in terms of run-off coefficient. The augmentation of water resource is proposed by the construction of rainwater harvesting structures like check dam, percolation pond, farm pond and gully check dam. The site suitability for different water harvesting structures is determined by considering spatially varying parameters like slope, infiltration, run-off potential, landuse/land cover, stream order, soil texture, land capability class, hydrological soil group and micro-watershed area. The determined suitable site has been validated with existing recharge structures of the study area. Accuracy assessment of the suitable sites for recharge structures potential maps of the Bindra watershed is 82.60%. Numéro de notice : A2017-674 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1213772 En ligne : https://doi.org/10.1080/10106049.2016.1213772 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87177 [article]

Identifying geospatial services across heterogeneous taxonomies / Anand Mehta in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)  

Public [article]  inGeocarto international > Vol 31 n° 9 - 10 (October - November 2016) . - pp 1058 - 1077 Titre : Identifying geospatial services across heterogeneous taxonomies Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Akash Ashapure, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 1058 - 1077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées [Termes IGN] identification automatique [Termes IGN] informatique [Termes IGN] service fondé sur la position [Termes IGN] taxinomie Résumé : (auteur) Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach. Numéro de notice : A2016-674 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1110208 Date de publication en ligne : 02/12/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1110208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81924 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Identification and utilization of land-use type importance for land-use data generalization / Wenxiu Gao in Cartographic journal (the), Vol 53 n° 1 (February 2016)  

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Designing identifiers: The approach in France in general and at IGN France in particular [diaporama] / Marie Lambois (2016) 

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Impacts of species misidentification on species distribution modeling with presence-only data / Hugo Costa in ISPRS International journal of geo-information, vol 4 n°4 (December 2015)  

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Automatic identification of building types based on topographic databases – a comparison of different data sources / Robert Hecht in International journal of cartography, vol 1 n° 1 (August 2015)  

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Fully polarimetric synthetic aperture radar (SAR) processing for crop type identification / Gang Hong in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 2 (February 2015)  

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Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation / Aarne Hovi (2015)  

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An accurate and computationally efficient algorithm for ground peak identification in large footprint waveform LiDAR data / Wei Zhuang in ISPRS Journal of photogrammetry and remote sensing, vol 95 (September 2014)  

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Demarcating new boundaries: mapping virtual polycentric communities through social media content / Anthony Stefanidis in Cartography and Geographic Information Science, vol 40 n° 2 (March 2013)  

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Identification of styles in topographic maps / Jérémie Ory (August 2013)    

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Automatic fuzzy clustering using modified differential evolution for image classification / U. Maulik in IEEE Transactions on geoscience and remote sensing, vol 48 n° 9 (September 2010)  

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