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Design and development 3D RRR model for Turkish cadastral system using international standards / Mehmet Alkan in Survey review, Vol 53 n° 379 (July 2021)  

Public [article]  inSurvey review > Vol 53 n° 379 (July 2021) . - pp 312 - 324 Titre : Design and development 3D RRR model for Turkish cadastral system using international standards Type de document : Article/Communication Auteurs : Mehmet Alkan, Auteur ; Hicret Gürsoy Sürmeneli, Auteur ; Zeynel Abidin Polat, Auteur Année de publication : 2021 Article en page(s) : pp 312 - 324 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger [Termes IGN] base de données foncières [Termes IGN] cadastre 3D [Termes IGN] infrastructure nationale des données localisées [Termes IGN] INSPIRE [Termes IGN] norme ISO [Termes IGN] standard OGC [Termes IGN] Turquie Résumé : (auteur) The concepts of three-dimensional cadastre (3D) and property ownership led to increased interest in land use management and research towards the end of the 90s. Within the scope of these studies, international standards and definitions have been realised. In Turkey, there are some academic studies available. However, there are not many studies conducted on an institutional basis. Turkey cadastre carried out by the General Directorate of Land Registry, and Cadastre (GDLRC) are kept. In this context, a 3D RRR (Right, Restriction and Responsibility) for Turkey-based cadastral data model design and development is essential in terms of not constitute a base for the study. The fact that these studies are in the context of LADM and ISO standards and OGC is very important in terms of the fact that the cadastral system is related to international standards. Numéro de notice : A2021-521 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1758386 Date de publication en ligne : 12/05/2020 En ligne : https://doi.org/10.1080/00396265.2020.1758386 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97954 [article]

Implementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI / Arif Cagdas Aydinoglu in Survey review, Vol 53 n° 379 (July 2021)  

Public [article]  inSurvey review > Vol 53 n° 379 (July 2021) . - pp 349 - 365 Titre : Implementing a mass valuation application on interoperable land valuation data model designed as an extension of the national GDI Type de document : Article/Communication Auteurs : Arif Cagdas Aydinoglu, Auteur ; Rabia Bovkir, Auteur ; Ismail Colkesen, Auteur Année de publication : 2021 Article en page(s) : pp 349 - 365 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] apprentissage automatique [Termes IGN] base de données foncières [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] coefficient de corrélation [Termes IGN] implémentation (informatique) [Termes IGN] infrastructure nationale des données localisées [Termes IGN] interopérabilité [Termes IGN] Istanbul (Turquie) [Termes IGN] métadonnées [Termes IGN] système d'information géographique Résumé : (auteur) The main purpose of this study is to propose an interoperable land valuation data model for residential properties as an extension of the national geographic data infrastructure (GDI) and to make mass valuation process applicable with the use of machine learning approach. As an example, random forest (RF) ensemble algorithm was implemented in Pendik district of Istanbul to evaluate the prediction performance by using thematic datasets compatible with the data model. This study provides a methodology for various urban applications and robustness of the algorithm increases the prediction of the real estate values with the use of qualified datasets. Numéro de notice : A2021-523 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1771967 Date de publication en ligne : 06/06/2020 En ligne : https://doi.org/10.1080/00396265.2020.1771967 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97960 [article]

An adaptive filtering algorithm of multilevel resolution point cloud / Youyuan Li in Survey review, Vol 53 n° 379 (July 2021)  

Public [article]  inSurvey review > Vol 53 n° 379 (July 2021) . - pp 300 - 311 Titre : An adaptive filtering algorithm of multilevel resolution point cloud Type de document : Article/Communication Auteurs : Youyuan Li, Auteur ; Jian Wang, Auteur ; Bin Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 300 - 311 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de filtrage [Termes IGN] analyse multirésolution [Termes IGN] classification ascendante hiérarchique [Termes IGN] données lidar [Termes IGN] filtrage de points [Termes IGN] filtre adaptatif [Termes IGN] interpolation spatiale [Termes IGN] Kappa de Cohen [Termes IGN] octree [Termes IGN] pente [Termes IGN] semis de points [Termes IGN] seuillage de points Résumé : (auteur) The existing filtering methods for airborne LiDAR point cloud have low accuracy. An adaptive filtering algorithm is proposed which is improved based on multilevel resolution algorithm. First double index structure of Octree and KDtree is established. Then the initial reference surface is constructed by ground seed points. According to the slope fluctuation situation, the grid resolution of the ground referential surface is adjusted in an adaptive way. Finally, the refined surface is formed gradually by multilevel renewing resolution to provide filtered point cloud with high accuracy. Experimental results show that the error of Type II can be effectively reduced, the average Kappa coefficient increases by 0.53% and the average total error decreases by 0.44% compared with multiresolution hierarchical classification algorithm. The result tested by practically measured data shows that Kappa coefficient can reach 90%. Especially, it maintains advantages of high accuracy under complex topographic environment. Numéro de notice : A2021-544 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2020.1755163 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1080/00396265.2020.1755163 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98042 [article]