Centre de documentation
scientifique

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]

Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (January 2020)  

Public [article]  inJournal of geodetic science > vol 10 n° 1 (January 2020) . - pp 14 - 22 Titre : Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery Type de document : Article/Communication Auteurs : H. Tombul, Auteur ; Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2020 Article en page(s) : pp 14 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] analyse canonique [Termes IGN] analyse comparative [Termes IGN] bande spectrale [Termes IGN] boosting adapté [Termes IGN] carte de la végétation [Termes IGN] carte thématique [Termes IGN] classification orientée objet [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] image Sentinel-MSI [Termes IGN] jeu de données [Termes IGN] Populus (genre) [Termes IGN] précision de la classification [Termes IGN] Rotation Forest classification [Termes IGN] segmentation multi-échelle [Termes IGN] Turquie Résumé : (auteur) The poplar species in the forest ecosystems are one of the most valuable and beneficial species for the society and environment. Conventional methods require high cost, time and labor need, and the results obtained vary and are insu˚cient in terms of achieved accuracy level. Determination of poplar cultivated fields and mapping of their spatial sites play a vital role for decision-makers and planners to enhance the economic and ecological value of poplar trees. The study aims to map Poplar (P. deltoides) cultivated areas in Akyazi district of Sakarya, Turkey province using various combinations of the Sentinel-2A image bands. For this purpose, object-based classification based on multi-resolution segmentation algorithm was utilized to produce image objects and ensemble learning algorithms, namely, Adaboost (AdaB), Random Forest (RF), Rotation Forest (RotFor) and Canonical correlation forest (CCF) were applied to produce thematic maps. In order to analyze the effects of the spectral bands of the Sentinel-2A image on the object-based classification performance, three datasets consisting of different spectral band combinations (i.e. four 10 m bands, six 20 m bands and ten 10m pan-sharpened bands) were used. The results showed that the RotFor and CCF classifiers produced superior classification performances compared to the AdaB and RF classifiers for the band combinations regarded in this study. Moreover, it was found that determination of poplar tree class level accuracy reached to ~94% in terms of F-score. It was also observed that the inclusion of the six spectral bands at 20 m resolution resulted in a noteworthy increase in classification accuracy (up to 6%) compared to single 10m band combination. Numéro de notice : A2020-420 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2020-0003 Date de publication en ligne : 04/05/2020 En ligne : https://doi.org/10.1515/jogs-2020-0003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95477 [article]

The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery / Ismail Colkesen in Geocarto international, vol 32 n° 1 (January 2017)  

Public [article]  inGeocarto international > vol 32 n° 1 (January 2017) . - pp 71 - 86 Titre : The use of logistic model tree (LMT) for pixel- and object-based classifications using high-resolution WorldView-2 imagery Type de document : Article/Communication Auteurs : Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2017 Article en page(s) : pp 71 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] arbre de décision [Termes IGN] classification orientée objet [Termes IGN] classification par arbre de décision [Termes IGN] classification pixellaire [Termes IGN] image Worldview [Termes IGN] régression logistique Résumé : (auteur) Logistic model tree (LMT), a new method integrating standard decision tree (DT) induction and linear logistic regression algorithm in a single tree, have been recently proposed as an alternative to DT-based learning algorithms. In this study, the LMT was applied in the context of pixel- and object-based classifications using high-resolution WorldView-2 imagery, and its performance was compared with C4.5, random forest and Adaboost. Results of the study showed that the LMT generally produced more accurate classification results than the other methods for both pixel- and object-based classifications. The improvement in classification accuracy reached to 3% in pixel-based and 5% in object-based classifications. It was also estimated that the LMT algorithm produced the most accurate results considering the allocation and overall disagreement errors. Based on the Wilcoxon’s Signed-Ranks tests, the performance differences between the LMT and the other methods were statistically significant for both pixel- and object-based image classifications. Numéro de notice : A2017-085 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1128486 Date de publication en ligne : 12/01/2016 En ligne : http://dx.doi.org/10.1080/10106049.2015.1128486 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84410 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2017011	RAB	Revue	Centre de documentation	En réserve L003	Disponible