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001-es BibID:BIBFORM109207
035-os BibID:(cikkazonosító)e14045 (Scopus)85149799289
Első szerző:Abriha Dávid (geográfus)
Cím:Smaller is better? Unduly nice accuracy assessments in roof detection using remote sensing data with machine learning and k-fold cross-validation / Dávid Abriha, Prashant K. Srivastava, Szilárd Szabó
Dátum:2023
ISSN:2405-8440
Megjegyzések:Deriving the thematic accuracy of models is a fundamental part of image classification analyses. K-fold cross-validation (KCV), as an accuracy assessment technique, can be biased because existing built-in algorithms of software solutions do not handle the high autocorrelation of remotely sensed images, leading to overestimation of accuracies. We aimed to quantify the magnitude of the overestimation of KCV-based accuracies and propose a method to overcome this problem with the example of rooftops using a WorldView-2 (WV2) satellite image, and two orthophotos. Random split to training/testing subsets, independent testing and different types of repeated KCV sampling strategies were used to generate input datasets for classification. Results revealed that applying the random splitting of reference data to training/testing subsets and KCV methods had significantly biased the accuracies by up to 17%; overall accuracies (OAs) can incorrectly reach >99%. We found that repeated KCV can provide similar results to independent testing when spatial sampling is applied with a sufficiently large distance threshold (in our case 10 m). Coarser resolution of WV2 ensured more reliable results (up to a 5?9% increase in OA) than orthophotos. Object-based pixel purity of buildings showed that when using a majority filter for at least of 50% of objects the final accuracy approached 100% with each sampling method. The final conclusion is that KCV-based modelling ensures better accuracy than single models (with better pixel purity on the object level), but the accuracy metrics without spatially filtered sampling are not reliable.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Urban environment
Roof classification
Accuracy assessment
Salt-and-pepper effect
Post-classification
Object-based pixel purity
Megjelenés:Heliyon. - 9 : 3 (2023), e14045, p.1-17. -
További szerzők:Srivastava, Prashant K. Szabó Szilárd (1974-) (geográfus)
Pályázati támogatás:Kooperatív Doktori Program
Egyéb
NKFI K 138079
Egyéb
NKFI K 142121
Egyéb
TKP2020-NKA-04
Egyéb
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DOI
Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM108565
035-os BibID:(cikkazonosító)e13406 (WoS)000968520300001 (Scopus)85147832924
Első szerző:Farkas Jenő Zsolt
Cím:A systematic review of urban green space research over the last 30 years : A bibliometric analysis / Jenő Zsolt Farkas, Edit Hoyk, Mariana Batista de Morais, György Csomós
Dátum:2023
ISSN:2405-8440
Megjegyzések:Worldwide, due to rapid urbanization, the provision of urban green spaces (UGSs) has become a primary goal of urban planning. As such, research on the benefits, effects, and challenges of UGSs has gained widespread attention among scholars. This paper comprehensively analyzes three decades of UGS research and its evolution; it conducts a bibliometric analysis of approximately 4000 articles and reviews from the Web of Science platform to discover the patterns and trends characterizing UGS research over time. We found that the pioneers of initial UGS research were the United States and Canada, whereas recently the European Union and China have become the global engines of research in the field. UGS research initially focused on studying urban forests, gradually shifting toward green spaces located in inner urban areas. Early on, researchers investigated UGSs (i.e., urban forests) from an ecological perspective. However, the most current research phase focuses on the social aspects of UGSs, characterized by such keywords as environmental justice and accessibility. Furthermore, the introduction of geographic information systems (GIS) has given new impetus to the evolution of UGS research and has remained the most used technological advancement besides remote sensing techniques. As the social aspects of UGS research have gained importance, new research methods have been employed, such as machine learning, big data and social media data analysis, and artificial intelligence, most recently.
Tárgyszavak:Természettudományok Földtudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Urban green space
Urban forest
Bibliometric analysis
Keyword cluster
Keyword burst
VOS Viewer
Megjelenés:Heliyon. - 9 : 2 (2023), p. 7-14. -
További szerzők:Hoyk Edit de Morais, Mariana Batista Csomós György (1974-) (geográfus)
Pályázati támogatás:142121
OTKA
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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