Geodetski vestnik . vol 63 n° 3Paru le : 01/09/2019 |
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Code-barres | Cote | Support | Localisation | Section | Disponibilité |
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139-2019031 | RAB | Revue | Centre de documentation | En réserve L003 | Disponible |
Dépouillements
Ajouter le résultat dans votre panierDelineation of vacant building land using orthophoto and lidar data object classification / Dejan Jenko in Geodetski vestnik, vol 63 n° 3 (September - November 2019)
[article]
Titre : Delineation of vacant building land using orthophoto and lidar data object classification Type de document : Article/Communication Auteurs : Dejan Jenko, Auteur ; Mojca Foški, Auteur ; Krištof Oštir, Auteur ; Žiga Kokalj, Auteur Année de publication : 2019 Article en page(s) : pp 344 - 378 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification orientée objet
[Termes IGN] couche thématique
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] logement
[Termes IGN] orthoimage
[Termes IGN] SlovénieRésumé : (Auteur) Exact data about the location and area of vacant building land have been a major issue in several Slovene municipalities. This article deals with automatic vacant building land delineation. The presented methodology is based on the object-based classification that derives the land cover layer from orthophoto and laser scanning data. With post-processing and data cleaning in GIS, we create the vacant building land layer. The methodology was tested in study areas in the Municipality of Trebnje. The results were compared to the vacant building land layer generated by visual interpretation (manual vectorisation). We found that the presented methodology of automatic delineation of vacant buildings can speed up the processing and lower the cost of manual vectorisation and, in particular, data updating but we cannot completely replace manual work. Numéro de notice : A2019-500 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.344-378 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.344-378 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93782
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 344 - 378[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 139-2019031 RAB Revue Centre de documentation En réserve L003 Disponible Change detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods / Jiří Šandera in Geodetski vestnik, vol 63 n° 3 (September - November 2019)
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Titre : Change detection work-flow for mapping changes from arable lands to permanent grasslands with advanced boosting methods Type de document : Article/Communication Auteurs : Jiří Šandera, Auteur ; Přemysl Štych, Auteur Année de publication : 2019 Article en page(s) : pp 379 - 394 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] boosting adapté
[Termes IGN] carte d'occupation du sol
[Termes IGN] chaîne de traitement
[Termes IGN] changement d'occupation du sol
[Termes IGN] détection de changement
[Termes IGN] image Landsat
[Termes IGN] prairie
[Termes IGN] terre arableRésumé : (Auteur) The necessity of mapping changes in land cover categories based on satellite imageries is a challenging task especially in terms of arable land and grasslands. The phenological phases of arable lands change quickly while grasslands is more stable. It might be hard to capture these changes regarding the spectral overlap between crops in full growth and grass itself. We have introduced a relatively simple processing workflow with good efficiency and accuracy. Our proposed method utilises the combination of a Multivariate Alteration Change Detection Algorithm and an existing boosting method, such as the AdaBoost algorithm with different weak learners and the most recent one – Extreme Gradient Boosting that is actually a relatively new approach in remote sensing. According to the results, the highest overall accuracy is 89.51 %. The proposed process workflow was tested on Landsat data with 30 m spatial resolution, using open-source software: R and GRASS GIS, Orfeo Toolbox library. Numéro de notice : A2019-501 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.379-394 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.379-394 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93783
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 379 - 394[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 139-2019031 RAB Revue Centre de documentation En réserve L003 Disponible Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)
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Titre : Comparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey Type de document : Article/Communication Auteurs : Baris Suleymanoglu, Auteur ; Metin Soycan, Auteur Année de publication : 2019 Article en page(s) : pp 395 - 414 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de filtrage
[Termes IGN] analyse comparative
[Termes IGN] convolution (signal)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] semis de points
[Termes IGN] test de performance
[Termes IGN] TurquieRésumé : (Auteur) A light detection and ranging (lidar) system is one of the most important technologies used for generating digital terrain models (DTMs). The point cloud data obtained by these systems consist of data gathered from ground and nonground features. To create a DTM with high resolution and accuracy, ground and nonground data must be separated. Numerous filtering algorithms have been developed for this purpose. The aim of this study was testing the filtering performance of six different filtering algorithms in four different test areas with different land cover were selected that had topographical features and characteristics. The algorithms were adaptive triangulated irregular network (ATIN), elevation threshold with an expand window (ETEW), maximum local slope (MLS), progressive morphology (PM), iterative polynomial fitting (IPF), and multiscale curvature classification (MCC) algorithms. In the results, all the filters performed well on a smooth surface and produced more errors in complex urban areas and rough terrain with dense vegetation. The IPF filtering algorithm generated the best results for the first three test areas (smooth landscape, urban areas and agricultural areas), while ETEW performed best in the fourth test area (steep areas with dense vegetation and infrastructure). Numéro de notice : A2019-502 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.15292/geodetski-vestnik.2019.03.395-414 En ligne : http://dx.doi.org/10.15292/geodetski-vestnik.2019.03.395-414 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93784
in Geodetski vestnik > vol 63 n° 3 (September - November 2019) . - pp 395 - 414[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 139-2019031 RAB Revue Centre de documentation En réserve L003 Disponible