FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3175322496> ?p ?o ?g. }

• W3175322496 endingPage "53" @default.
• W3175322496 startingPage "47" @default.
• W3175322496 abstract "Abstract. The use of remote sensing data for burned area mapping hast led to unprecedented advances within the field in recent years. Although threshold and traditional machine learning based methods have successfully been applied to the task, they implicate drawbacks including the involvement of complex rule sets and requirement of previous feature engineering. In contrast, deep learning offers an end-to-end solution for image analysis and semantic segmentation. In this study, a variation of U-Net is investigated for mapping burned areas in mono-temporal Sentinel-2 imagery. The experimental setup is divided into two phases. The first one includes a performance evaluation based on test data, while the second serves as a use case simulation and spatial evaluation of training data quality. The former is especially designed to compare the results between two local (trained only with data from the respective research areas) and a global (trained with the whole dataset) variant of the model with research areas being Indonesia and Central Africa. The networks are trained from scratch with a manually generated customized training dataset. The application of the two variants per region revealed only slight superiority of the local model (macro-F1: 92%) over the global model (macro-F1: 91%) in Indonesia with no difference in overall accuracy (OA) at 94%. In Central Africa, the results of the global and local model are the same in both metrics (OA: 84%, macro-F1: 82%). Overall, the outcome demonstrates the global model’s ability to generalize despite high dissimilarities between the research areas." @default.
• W3175322496 created "2021-07-05" @default.
• W3175322496 creator A5053347768 @default.
• W3175322496 creator A5071347836 @default.
• W3175322496 date "2021-06-28" @default.
• W3175322496 modified "2023-10-18" @default.
• W3175322496 title "SEMANTIC SEGMENTATION OF BURNED AREAS IN SATELLITE IMAGES USING A U-NET-BASED CONVOLUTIONAL NEURAL NETWORK" @default.
• W3175322496 cites W1901129140 @default.
• W3175322496 cites W1923314171 @default.
• W3175322496 cites W2089501297 @default.
• W3175322496 cites W2103219856 @default.
• W3175322496 cites W2112796928 @default.
• W3175322496 cites W2165868425 @default.
• W3175322496 cites W2282323584 @default.
• W3175322496 cites W2400971706 @default.
• W3175322496 cites W2504108613 @default.
• W3175322496 cites W2510487214 @default.
• W3175322496 cites W2573821892 @default.
• W3175322496 cites W2603092380 @default.
• W3175322496 cites W2770853283 @default.
• W3175322496 cites W2773264969 @default.
• W3175322496 cites W2920767026 @default.
• W3175322496 cites W2964054038 @default.
• W3175322496 cites W2998663874 @default.
• W3175322496 cites W2999453397 @default.
• W3175322496 cites W3045606376 @default.
• W3175322496 cites W3046145841 @default.
• W3175322496 cites W3112847460 @default.
• W3175322496 cites W3156610545 @default.
• W3175322496 doi "https://doi.org/10.5194/isprs-archives-xliii-b3-2021-47-2021" @default.
• W3175322496 hasPublicationYear "2021" @default.
• W3175322496 type Work @default.
• W3175322496 sameAs 3175322496 @default.
• W3175322496 citedByCount "11" @default.
• W3175322496 countsByYear W31753224962021 @default.
• W3175322496 countsByYear W31753224962022 @default.
• W3175322496 countsByYear W31753224962023 @default.
• W3175322496 crossrefType "journal-article" @default.
• W3175322496 hasAuthorship W3175322496A5053347768 @default.
• W3175322496 hasAuthorship W3175322496A5071347836 @default.
• W3175322496 hasBestOaLocation W31753224961 @default.
• W3175322496 hasConcept C108583219 @default.
• W3175322496 hasConcept C119857082 @default.
• W3175322496 hasConcept C124101348 @default.
• W3175322496 hasConcept C127413603 @default.
• W3175322496 hasConcept C138885662 @default.
• W3175322496 hasConcept C146978453 @default.
• W3175322496 hasConcept C148524875 @default.
• W3175322496 hasConcept C153180895 @default.
• W3175322496 hasConcept C154945302 @default.
• W3175322496 hasConcept C162324750 @default.
• W3175322496 hasConcept C166955791 @default.
• W3175322496 hasConcept C187736073 @default.
• W3175322496 hasConcept C19269812 @default.
• W3175322496 hasConcept C199360897 @default.
• W3175322496 hasConcept C202444582 @default.
• W3175322496 hasConcept C2776401178 @default.
• W3175322496 hasConcept C2778827112 @default.
• W3175322496 hasConcept C2780451532 @default.
• W3175322496 hasConcept C33923547 @default.
• W3175322496 hasConcept C41008148 @default.
• W3175322496 hasConcept C41895202 @default.
• W3175322496 hasConcept C50644808 @default.
• W3175322496 hasConcept C81363708 @default.
• W3175322496 hasConcept C89600930 @default.
• W3175322496 hasConcept C9652623 @default.
• W3175322496 hasConceptScore W3175322496C108583219 @default.
• W3175322496 hasConceptScore W3175322496C119857082 @default.
• W3175322496 hasConceptScore W3175322496C124101348 @default.
• W3175322496 hasConceptScore W3175322496C127413603 @default.
• W3175322496 hasConceptScore W3175322496C138885662 @default.
• W3175322496 hasConceptScore W3175322496C146978453 @default.
• W3175322496 hasConceptScore W3175322496C148524875 @default.
• W3175322496 hasConceptScore W3175322496C153180895 @default.
• W3175322496 hasConceptScore W3175322496C154945302 @default.
• W3175322496 hasConceptScore W3175322496C162324750 @default.
• W3175322496 hasConceptScore W3175322496C166955791 @default.
• W3175322496 hasConceptScore W3175322496C187736073 @default.
• W3175322496 hasConceptScore W3175322496C19269812 @default.
• W3175322496 hasConceptScore W3175322496C199360897 @default.
• W3175322496 hasConceptScore W3175322496C202444582 @default.
• W3175322496 hasConceptScore W3175322496C2776401178 @default.
• W3175322496 hasConceptScore W3175322496C2778827112 @default.
• W3175322496 hasConceptScore W3175322496C2780451532 @default.
• W3175322496 hasConceptScore W3175322496C33923547 @default.
• W3175322496 hasConceptScore W3175322496C41008148 @default.
• W3175322496 hasConceptScore W3175322496C41895202 @default.
• W3175322496 hasConceptScore W3175322496C50644808 @default.
• W3175322496 hasConceptScore W3175322496C81363708 @default.
• W3175322496 hasConceptScore W3175322496C89600930 @default.
• W3175322496 hasConceptScore W3175322496C9652623 @default.
• W3175322496 hasLocation W31753224961 @default.
• W3175322496 hasOpenAccess W3175322496 @default.
• W3175322496 hasPrimaryLocation W31753224961 @default.
• W3175322496 hasRelatedWork W2738221750 @default.
• W3175322496 hasRelatedWork W2760085659 @default.
• W3175322496 hasRelatedWork W2795329967 @default.
• W3175322496 hasRelatedWork W3008173435 @default.

• next