Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387560574> ?p ?o ?g. }
Showing items 1 to 71 of
71
with 100 items per page.
- W4387560574 abstract "With changing climatic conditions, we are already seeing an increase in extreme weather events and their secondary consequences, including landslides. Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. However, these both rely on data from previous landslide events, which is often scarce. Many deep learning (DL) models have recently been applied for landside mapping using medium- to high-resolution satellite images as input. However, they often suffer from sensitivity problems, overfitting, and low mapping accuracy. This study addresses some of these limitations by using a diverse global landslide dataset, using different segmentation models, such as Unet, Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an ensemble model. The ensemble model achieved the highest F1-score (0.69) when combining both Sentinel-1 and Sentinel-2 bands, with the highest average improvement of 6.87 % when the ensemble size was 20. On the other hand, Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the ensemble size is 20 with an improvement of 14.59 % when the ensemble size is 20. This result shows considerable potential in building a robust and reliable monitoring system based on changes in vegetation index dNDVI only." @default.
- W4387560574 created "2023-10-12" @default.
- W4387560574 creator A5013648849 @default.
- W4387560574 creator A5014402882 @default.
- W4387560574 creator A5059764775 @default.
- W4387560574 creator A5081994202 @default.
- W4387560574 date "2023-09-12" @default.
- W4387560574 modified "2023-10-13" @default.
- W4387560574 title "Automating global landslide detection with heterogeneous ensemble deep-learning classification" @default.
- W4387560574 doi "https://doi.org/10.48550/arxiv.2310.05959" @default.
- W4387560574 hasPublicationYear "2023" @default.
- W4387560574 type Work @default.
- W4387560574 citedByCount "0" @default.
- W4387560574 crossrefType "posted-content" @default.
- W4387560574 hasAuthorship W4387560574A5013648849 @default.
- W4387560574 hasAuthorship W4387560574A5014402882 @default.
- W4387560574 hasAuthorship W4387560574A5059764775 @default.
- W4387560574 hasAuthorship W4387560574A5081994202 @default.
- W4387560574 hasBestOaLocation W43875605741 @default.
- W4387560574 hasConcept C108583219 @default.
- W4387560574 hasConcept C119857082 @default.
- W4387560574 hasConcept C119898033 @default.
- W4387560574 hasConcept C127313418 @default.
- W4387560574 hasConcept C154945302 @default.
- W4387560574 hasConcept C178790620 @default.
- W4387560574 hasConcept C185592680 @default.
- W4387560574 hasConcept C186295008 @default.
- W4387560574 hasConcept C187320778 @default.
- W4387560574 hasConcept C205649164 @default.
- W4387560574 hasConcept C22019652 @default.
- W4387560574 hasConcept C29825287 @default.
- W4387560574 hasConcept C41008148 @default.
- W4387560574 hasConcept C45942800 @default.
- W4387560574 hasConcept C49261128 @default.
- W4387560574 hasConcept C50644808 @default.
- W4387560574 hasConcept C62649853 @default.
- W4387560574 hasConcept C76155785 @default.
- W4387560574 hasConceptScore W4387560574C108583219 @default.
- W4387560574 hasConceptScore W4387560574C119857082 @default.
- W4387560574 hasConceptScore W4387560574C119898033 @default.
- W4387560574 hasConceptScore W4387560574C127313418 @default.
- W4387560574 hasConceptScore W4387560574C154945302 @default.
- W4387560574 hasConceptScore W4387560574C178790620 @default.
- W4387560574 hasConceptScore W4387560574C185592680 @default.
- W4387560574 hasConceptScore W4387560574C186295008 @default.
- W4387560574 hasConceptScore W4387560574C187320778 @default.
- W4387560574 hasConceptScore W4387560574C205649164 @default.
- W4387560574 hasConceptScore W4387560574C22019652 @default.
- W4387560574 hasConceptScore W4387560574C29825287 @default.
- W4387560574 hasConceptScore W4387560574C41008148 @default.
- W4387560574 hasConceptScore W4387560574C45942800 @default.
- W4387560574 hasConceptScore W4387560574C49261128 @default.
- W4387560574 hasConceptScore W4387560574C50644808 @default.
- W4387560574 hasConceptScore W4387560574C62649853 @default.
- W4387560574 hasConceptScore W4387560574C76155785 @default.
- W4387560574 hasLocation W43875605741 @default.
- W4387560574 hasOpenAccess W4387560574 @default.
- W4387560574 hasPrimaryLocation W43875605741 @default.
- W4387560574 hasRelatedWork W1038900426 @default.
- W4387560574 hasRelatedWork W1807784185 @default.
- W4387560574 hasRelatedWork W1909207154 @default.
- W4387560574 hasRelatedWork W2794896638 @default.
- W4387560574 hasRelatedWork W3101614107 @default.
- W4387560574 hasRelatedWork W3124390867 @default.
- W4387560574 hasRelatedWork W3149839747 @default.
- W4387560574 hasRelatedWork W3202800081 @default.
- W4387560574 hasRelatedWork W3204228978 @default.
- W4387560574 hasRelatedWork W45170056 @default.
- W4387560574 isParatext "false" @default.
- W4387560574 isRetracted "false" @default.
- W4387560574 workType "article" @default.