FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3154205514> ?p ?o ?g. }

• W3154205514 endingPage "14976" @default.
• W3154205514 startingPage "14960" @default.
• W3154205514 abstract "Light Detection and Ranging (LiDAR), an active remote sensing technology, is becoming an essential tool for geoinformation extraction and urban planning. Airborne Laser Scanning (ALS) point clouds segmentation and accurate classification are challenging and crucial to produce different geo-information products like three-dimensional (3D) city designs. This paper introduces an effective data-driven approach to build roof superstructures classification for airborne LiDAR point clouds with very low density and imbalanced classes, covering an urban area. Notably, it focuses on building roof superstructures (especially dormers and chimneys) and mitigating nonplanar objects' problems. Also, the imbalanced class problem of LiDAR data, to the best of our knowledge, is not yet addressed in the literature; it is considered in this study. The major advantage of the proposed approach is using only raw data without assumptions on the distribution underlying data. The main methodological novelties of this work are summarized in the following key elements. (i) At first, an adapted connected component analysis for 3D points cloud is proposed. (ii) Twelve geometry-based features are extracted for each component. (iii) A Support Vector Machine (SVM)-driven procedure is applied to classify the 3D components. (iv) Furthermore, a new component size-based sampling (CSBS) method is proposed to treat the imbalanced data problem and has been compared with several existing resampling strategies. In this study, components are classified into five classes: shed and gable dormers, chimneys, ground, and others. The results of this investigation show the satisfying classification performance of the proposed approach. Results also showed that the proposed approach outperformed machine learning methods, including SVM, Random Forest, Decision Tree, and Adaboost." @default.
• W3154205514 created "2021-04-26" @default.
• W3154205514 creator A5024067001 @default.
• W3154205514 creator A5024155508 @default.
• W3154205514 creator A5047331654 @default.
• W3154205514 creator A5077059233 @default.
• W3154205514 creator A5086034050 @default.
• W3154205514 creator A5087572406 @default.
• W3154205514 date "2021-07-01" @default.
• W3154205514 modified "2023-10-04" @default.
• W3154205514 title "Building Roof Superstructures Classification From Imbalanced and Low Density Airborne LiDAR Point Cloud" @default.
• W3154205514 cites W1973644502 @default.
• W3154205514 cites W1977271893 @default.
• W3154205514 cites W1983097330 @default.
• W3154205514 cites W1994699846 @default.
• W3154205514 cites W1994881920 @default.
• W3154205514 cites W2001014393 @default.
• W3154205514 cites W2006305514 @default.
• W3154205514 cites W2006983504 @default.
• W3154205514 cites W2015252931 @default.
• W3154205514 cites W2016068122 @default.
• W3154205514 cites W2020257492 @default.
• W3154205514 cites W2028238598 @default.
• W3154205514 cites W2030676057 @default.
• W3154205514 cites W2040861824 @default.
• W3154205514 cites W2041642242 @default.
• W3154205514 cites W2044835201 @default.
• W3154205514 cites W2059682476 @default.
• W3154205514 cites W2064765926 @default.
• W3154205514 cites W2076656703 @default.
• W3154205514 cites W2088805832 @default.
• W3154205514 cites W2104803300 @default.
• W3154205514 cites W2104933073 @default.
• W3154205514 cites W2123576693 @default.
• W3154205514 cites W2130722170 @default.
• W3154205514 cites W2131256165 @default.
• W3154205514 cites W2132791018 @default.
• W3154205514 cites W2133539573 @default.
• W3154205514 cites W2146864100 @default.
• W3154205514 cites W2148143831 @default.
• W3154205514 cites W2150089019 @default.
• W3154205514 cites W2151631165 @default.
• W3154205514 cites W2307988022 @default.
• W3154205514 cites W2330711204 @default.
• W3154205514 cites W2336012816 @default.
• W3154205514 cites W2395466322 @default.
• W3154205514 cites W2425812835 @default.
• W3154205514 cites W2517188359 @default.
• W3154205514 cites W2552163339 @default.
• W3154205514 cites W2560671110 @default.
• W3154205514 cites W2562874528 @default.
• W3154205514 cites W2576217372 @default.
• W3154205514 cites W2587360163 @default.
• W3154205514 cites W2609747629 @default.
• W3154205514 cites W2618534726 @default.
• W3154205514 cites W2742692613 @default.
• W3154205514 cites W2766296277 @default.
• W3154205514 cites W2769706653 @default.
• W3154205514 cites W2782155227 @default.
• W3154205514 cites W2789715252 @default.
• W3154205514 cites W2798886737 @default.
• W3154205514 cites W2801573647 @default.
• W3154205514 cites W2804937815 @default.
• W3154205514 cites W2885035168 @default.
• W3154205514 cites W2885739804 @default.
• W3154205514 cites W2887869984 @default.
• W3154205514 cites W2905401975 @default.
• W3154205514 cites W2911381541 @default.
• W3154205514 cites W2921362487 @default.
• W3154205514 cites W2953297071 @default.
• W3154205514 cites W2953367155 @default.
• W3154205514 cites W2953785039 @default.
• W3154205514 cites W2971090287 @default.
• W3154205514 cites W2972914098 @default.
• W3154205514 cites W2986401912 @default.
• W3154205514 cites W3048367569 @default.
• W3154205514 doi "https://doi.org/10.1109/jsen.2021.3073535" @default.
• W3154205514 hasPublicationYear "2021" @default.
• W3154205514 type Work @default.
• W3154205514 sameAs 3154205514 @default.
• W3154205514 citedByCount "7" @default.
• W3154205514 countsByYear W31542055142021 @default.
• W3154205514 countsByYear W31542055142022 @default.
• W3154205514 countsByYear W31542055142023 @default.
• W3154205514 crossrefType "journal-article" @default.
• W3154205514 hasAuthorship W3154205514A5024067001 @default.
• W3154205514 hasAuthorship W3154205514A5024155508 @default.
• W3154205514 hasAuthorship W3154205514A5047331654 @default.
• W3154205514 hasAuthorship W3154205514A5077059233 @default.
• W3154205514 hasAuthorship W3154205514A5086034050 @default.
• W3154205514 hasAuthorship W3154205514A5087572406 @default.
• W3154205514 hasBestOaLocation W31542055142 @default.
• W3154205514 hasConcept C115051666 @default.
• W3154205514 hasConcept C121332964 @default.
• W3154205514 hasConcept C12267149 @default.
• W3154205514 hasConcept C124101348 @default.
• W3154205514 hasConcept C131979681 @default.
• W3154205514 hasConcept C154945302 @default.

• next