FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3097134639> ?p ?o ?g. }

• W3097134639 endingPage "3537" @default.
• W3097134639 startingPage "3537" @default.
• W3097134639 abstract "Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic detection methods for this task that suffer from a high false alarm rate. To solve this problem, we propose a novel framework to detect undocumented building constructions using a Convolutional Neural Network (CNN) and official geodata, including high resolution optical data and the Normalized Digital Surface Model (nDSM). More specifically, an undocumented building pixel is labeled as “building” by the CNN but does not overlap with a building polygon of the DFK. The class of old or new undocumented building can be further separated when a Temporal Digital Surface Model (tDSM) is introduced in the stage of decision fusion. In a further step, undocumented story construction is detected as the pixels that are “building” in both DFK and predicted results from CNN, but shows a height deviation from the tDSM. By doing so, we have produced a seamless map of undocumented building constructions for one-quarter of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has proved that our framework is robust to detect undocumented building constructions at large-scale. Considering that the official geodata exploited in this research is advantageous because of its high quality and large coverage, a transferability analysis experiment is also designed in our research to investigate the sampling strategies for building detection at large-scale. Our results indicate that building detection results in unseen areas at large-scale can be improved when training samples are collected from different districts. In an area where training samples are available, local training sampless collection and training can save much time and effort." @default.
• W3097134639 created "2020-11-09" @default.
• W3097134639 creator A5012413938 @default.
• W3097134639 creator A5019995792 @default.
• W3097134639 creator A5040830361 @default.
• W3097134639 creator A5057407503 @default.
• W3097134639 creator A5058601965 @default.
• W3097134639 creator A5061007704 @default.
• W3097134639 creator A5075376095 @default.
• W3097134639 creator A5090588677 @default.
• W3097134639 date "2020-10-28" @default.
• W3097134639 modified "2023-10-15" @default.
• W3097134639 title "Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network" @default.
• W3097134639 cites W1966411833 @default.
• W3097134639 cites W2076131212 @default.
• W3097134639 cites W2326674917 @default.
• W3097134639 cites W2408023307 @default.
• W3097134639 cites W2538244214 @default.
• W3097134639 cites W2782522152 @default.
• W3097134639 cites W2792827505 @default.
• W3097134639 cites W2884821995 @default.
• W3097134639 cites W2888799854 @default.
• W3097134639 cites W2888889084 @default.
• W3097134639 cites W2898605096 @default.
• W3097134639 cites W2901582739 @default.
• W3097134639 cites W2904271136 @default.
• W3097134639 cites W2905332579 @default.
• W3097134639 cites W2908320224 @default.
• W3097134639 cites W2915971115 @default.
• W3097134639 cites W2943781490 @default.
• W3097134639 cites W2949351478 @default.
• W3097134639 cites W2954896312 @default.
• W3097134639 cites W2962949934 @default.
• W3097134639 cites W2963525222 @default.
• W3097134639 cites W2963881378 @default.
• W3097134639 cites W2977002487 @default.
• W3097134639 cites W2994434065 @default.
• W3097134639 cites W3005814862 @default.
• W3097134639 cites W3011868305 @default.
• W3097134639 cites W3014819688 @default.
• W3097134639 cites W3021057985 @default.
• W3097134639 cites W3100521496 @default.
• W3097134639 cites W3102112912 @default.
• W3097134639 doi "https://doi.org/10.3390/rs12213537" @default.
• W3097134639 hasPublicationYear "2020" @default.
• W3097134639 type Work @default.
• W3097134639 sameAs 3097134639 @default.
• W3097134639 citedByCount "7" @default.
• W3097134639 countsByYear W30971346392021 @default.
• W3097134639 countsByYear W30971346392022 @default.
• W3097134639 countsByYear W30971346392023 @default.
• W3097134639 crossrefType "journal-article" @default.
• W3097134639 hasAuthorship W3097134639A5012413938 @default.
• W3097134639 hasAuthorship W3097134639A5019995792 @default.
• W3097134639 hasAuthorship W3097134639A5040830361 @default.
• W3097134639 hasAuthorship W3097134639A5057407503 @default.
• W3097134639 hasAuthorship W3097134639A5058601965 @default.
• W3097134639 hasAuthorship W3097134639A5061007704 @default.
• W3097134639 hasAuthorship W3097134639A5075376095 @default.
• W3097134639 hasAuthorship W3097134639A5090588677 @default.
• W3097134639 hasBestOaLocation W30971346391 @default.
• W3097134639 hasConcept C124101348 @default.
• W3097134639 hasConcept C126042441 @default.
• W3097134639 hasConcept C134207755 @default.
• W3097134639 hasConcept C154945302 @default.
• W3097134639 hasConcept C160633673 @default.
• W3097134639 hasConcept C190694206 @default.
• W3097134639 hasConcept C205649164 @default.
• W3097134639 hasConcept C41008148 @default.
• W3097134639 hasConcept C58640448 @default.
• W3097134639 hasConcept C62649853 @default.
• W3097134639 hasConcept C76155785 @default.
• W3097134639 hasConcept C81363708 @default.
• W3097134639 hasConcept C9770341 @default.
• W3097134639 hasConceptScore W3097134639C124101348 @default.
• W3097134639 hasConceptScore W3097134639C126042441 @default.
• W3097134639 hasConceptScore W3097134639C134207755 @default.
• W3097134639 hasConceptScore W3097134639C154945302 @default.
• W3097134639 hasConceptScore W3097134639C160633673 @default.
• W3097134639 hasConceptScore W3097134639C190694206 @default.
• W3097134639 hasConceptScore W3097134639C205649164 @default.
• W3097134639 hasConceptScore W3097134639C41008148 @default.
• W3097134639 hasConceptScore W3097134639C58640448 @default.
• W3097134639 hasConceptScore W3097134639C62649853 @default.
• W3097134639 hasConceptScore W3097134639C76155785 @default.
• W3097134639 hasConceptScore W3097134639C81363708 @default.
• W3097134639 hasConceptScore W3097134639C9770341 @default.
• W3097134639 hasIssue "21" @default.
• W3097134639 hasLocation W30971346391 @default.
• W3097134639 hasLocation W30971346392 @default.
• W3097134639 hasLocation W30971346393 @default.
• W3097134639 hasLocation W30971346394 @default.
• W3097134639 hasOpenAccess W3097134639 @default.
• W3097134639 hasPrimaryLocation W30971346391 @default.
• W3097134639 hasRelatedWork W1899898439 @default.
• W3097134639 hasRelatedWork W1963842245 @default.
• W3097134639 hasRelatedWork W2017709107 @default.
• W3097134639 hasRelatedWork W2051591117 @default.

• next