FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3082733530> ?p ?o ?g. }

• W3082733530 endingPage "527" @default.
• W3082733530 startingPage "527" @default.
• W3082733530 abstract "With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research." @default.
• W3082733530 created "2020-09-08" @default.
• W3082733530 creator A5007897707 @default.
• W3082733530 creator A5016271984 @default.
• W3082733530 creator A5019519408 @default.
• W3082733530 creator A5034998190 @default.
• W3082733530 creator A5058458304 @default.
• W3082733530 creator A5064842058 @default.
• W3082733530 creator A5066901819 @default.
• W3082733530 creator A5078340301 @default.
• W3082733530 date "2020-09-02" @default.
• W3082733530 modified "2023-10-17" @default.
• W3082733530 title "Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework" @default.
• W3082733530 cites W1993307196 @default.
• W3082733530 cites W2023706704 @default.
• W3082733530 cites W2066416082 @default.
• W3082733530 cites W2068323376 @default.
• W3082733530 cites W2099839695 @default.
• W3082733530 cites W2109255472 @default.
• W3082733530 cites W2119879130 @default.
• W3082733530 cites W2121885753 @default.
• W3082733530 cites W2139514605 @default.
• W3082733530 cites W2183182206 @default.
• W3082733530 cites W2263939288 @default.
• W3082733530 cites W2336879049 @default.
• W3082733530 cites W2412782625 @default.
• W3082733530 cites W2501814412 @default.
• W3082733530 cites W2557186959 @default.
• W3082733530 cites W2604086375 @default.
• W3082733530 cites W2618530766 @default.
• W3082733530 cites W2623490820 @default.
• W3082733530 cites W2737021924 @default.
• W3082733530 cites W2737836416 @default.
• W3082733530 cites W2761781479 @default.
• W3082733530 cites W2765739551 @default.
• W3082733530 cites W2767959320 @default.
• W3082733530 cites W2795478733 @default.
• W3082733530 cites W2803946774 @default.
• W3082733530 cites W2810004461 @default.
• W3082733530 cites W2903950532 @default.
• W3082733530 cites W2908968031 @default.
• W3082733530 cites W2911964244 @default.
• W3082733530 cites W2933353266 @default.
• W3082733530 cites W2943214363 @default.
• W3082733530 cites W2953915092 @default.
• W3082733530 cites W2972258720 @default.
• W3082733530 cites W2980006727 @default.
• W3082733530 cites W2991488782 @default.
• W3082733530 cites W2996331621 @default.
• W3082733530 cites W3036106951 @default.
• W3082733530 doi "https://doi.org/10.3390/ijgi9090527" @default.
• W3082733530 hasPublicationYear "2020" @default.
• W3082733530 type Work @default.
• W3082733530 sameAs 3082733530 @default.
• W3082733530 citedByCount "8" @default.
• W3082733530 countsByYear W30827335302021 @default.
• W3082733530 countsByYear W30827335302022 @default.
• W3082733530 countsByYear W30827335302023 @default.
• W3082733530 crossrefType "journal-article" @default.
• W3082733530 hasAuthorship W3082733530A5007897707 @default.
• W3082733530 hasAuthorship W3082733530A5016271984 @default.
• W3082733530 hasAuthorship W3082733530A5019519408 @default.
• W3082733530 hasAuthorship W3082733530A5034998190 @default.
• W3082733530 hasAuthorship W3082733530A5058458304 @default.
• W3082733530 hasAuthorship W3082733530A5064842058 @default.
• W3082733530 hasAuthorship W3082733530A5066901819 @default.
• W3082733530 hasAuthorship W3082733530A5078340301 @default.
• W3082733530 hasBestOaLocation W30827335301 @default.
• W3082733530 hasConcept C108583219 @default.
• W3082733530 hasConcept C127413603 @default.
• W3082733530 hasConcept C147176958 @default.
• W3082733530 hasConcept C154945302 @default.
• W3082733530 hasConcept C205649164 @default.
• W3082733530 hasConcept C2778755073 @default.
• W3082733530 hasConcept C2780648208 @default.
• W3082733530 hasConcept C39432304 @default.
• W3082733530 hasConcept C41008148 @default.
• W3082733530 hasConcept C4792198 @default.
• W3082733530 hasConcept C487182 @default.
• W3082733530 hasConcept C49545453 @default.
• W3082733530 hasConcept C58640448 @default.
• W3082733530 hasConcept C62649853 @default.
• W3082733530 hasConcept C81363708 @default.
• W3082733530 hasConcept C97931131 @default.
• W3082733530 hasConceptScore W3082733530C108583219 @default.
• W3082733530 hasConceptScore W3082733530C127413603 @default.
• W3082733530 hasConceptScore W3082733530C147176958 @default.
• W3082733530 hasConceptScore W3082733530C154945302 @default.
• W3082733530 hasConceptScore W3082733530C205649164 @default.
• W3082733530 hasConceptScore W3082733530C2778755073 @default.
• W3082733530 hasConceptScore W3082733530C2780648208 @default.
• W3082733530 hasConceptScore W3082733530C39432304 @default.
• W3082733530 hasConceptScore W3082733530C41008148 @default.
• W3082733530 hasConceptScore W3082733530C4792198 @default.
• W3082733530 hasConceptScore W3082733530C487182 @default.
• W3082733530 hasConceptScore W3082733530C49545453 @default.
• W3082733530 hasConceptScore W3082733530C58640448 @default.
• W3082733530 hasConceptScore W3082733530C62649853 @default.

• next