Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387026799> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W4387026799 endingPage "130248" @default.
- W4387026799 startingPage "130248" @default.
- W4387026799 abstract "Recent years have seen rapid progress in the adoption of fully convolutional neural networks (FCN) to classify optical satellite imagery, made possible by a combination of new FCN architectures, next-generation GPUs, and publicly available satellite imagery from, e.g., the Landsat and Sentinel missions. These satellites offer repeat global coverage at intervals of only a few days at a spatial resolution of ≥10 m, which is sufficient for some but not all applications of interest. A smaller body of literature considers similar tools to classify commercial satellite imagery that offer 1 – 2 orders of magnitude higher spatial resolutions but with limited spatial and temporal coverage. In this work, we develop a super-resolution FCN to achieve the best of both worlds: land cover classification at commercial-level spatial resolutions but with the spatiotemporal coverage of public satellite imagery. To do so, we label 1 – 2 m resolution commercial imagery and use this as training data for super-resolution FCN. As a specific application, we focus on the segmentation of rivers, with the goal of tracking changes in reach-averaged river widths, depths, and discharges over time. We present detailed performance analyses and demonstrate that, surprisingly, we achieve ≳ 90% classification accuracy at meter-scale resolutions from free Sentinel-2 imagery. We extensively validate our model through in situ USGS gage data and ground-truth GPS tracing of river shorelines. By making our super-resolution FCN codes and training weights publicly available, we hope that these tools will be of use to the broader hydrology community and beyond, as the models can be re-trained for other segmentation tasks." @default.
- W4387026799 created "2023-09-26" @default.
- W4387026799 creator A5004248197 @default.
- W4387026799 creator A5006213039 @default.
- W4387026799 creator A5033174571 @default.
- W4387026799 creator A5043044464 @default.
- W4387026799 creator A5068237537 @default.
- W4387026799 creator A5075797962 @default.
- W4387026799 creator A5091877085 @default.
- W4387026799 date "2023-11-01" @default.
- W4387026799 modified "2023-10-01" @default.
- W4387026799 title "Super-resolution deep neural networks for water classification from free multispectral satellite imagery" @default.
- W4387026799 cites W2021324314 @default.
- W4387026799 cites W2056435747 @default.
- W4387026799 cites W2194775991 @default.
- W4387026799 cites W2789283384 @default.
- W4387026799 cites W2964309882 @default.
- W4387026799 cites W3035028692 @default.
- W4387026799 cites W3046020299 @default.
- W4387026799 cites W3088253102 @default.
- W4387026799 cites W3094614841 @default.
- W4387026799 cites W3163388010 @default.
- W4387026799 cites W3176999192 @default.
- W4387026799 cites W3213555053 @default.
- W4387026799 cites W4221007166 @default.
- W4387026799 cites W4297533962 @default.
- W4387026799 cites W4317434882 @default.
- W4387026799 doi "https://doi.org/10.1016/j.jhydrol.2023.130248" @default.
- W4387026799 hasPublicationYear "2023" @default.
- W4387026799 type Work @default.
- W4387026799 citedByCount "0" @default.
- W4387026799 crossrefType "journal-article" @default.
- W4387026799 hasAuthorship W4387026799A5004248197 @default.
- W4387026799 hasAuthorship W4387026799A5006213039 @default.
- W4387026799 hasAuthorship W4387026799A5033174571 @default.
- W4387026799 hasAuthorship W4387026799A5043044464 @default.
- W4387026799 hasAuthorship W4387026799A5068237537 @default.
- W4387026799 hasAuthorship W4387026799A5075797962 @default.
- W4387026799 hasAuthorship W4387026799A5091877085 @default.
- W4387026799 hasConcept C108583219 @default.
- W4387026799 hasConcept C119666444 @default.
- W4387026799 hasConcept C121332964 @default.
- W4387026799 hasConcept C127313418 @default.
- W4387026799 hasConcept C127413603 @default.
- W4387026799 hasConcept C146849305 @default.
- W4387026799 hasConcept C146978453 @default.
- W4387026799 hasConcept C154945302 @default.
- W4387026799 hasConcept C173163844 @default.
- W4387026799 hasConcept C19269812 @default.
- W4387026799 hasConcept C205372480 @default.
- W4387026799 hasConcept C205649164 @default.
- W4387026799 hasConcept C2778102629 @default.
- W4387026799 hasConcept C2778755073 @default.
- W4387026799 hasConcept C41008148 @default.
- W4387026799 hasConcept C58640448 @default.
- W4387026799 hasConcept C62520636 @default.
- W4387026799 hasConcept C62649853 @default.
- W4387026799 hasConcept C81363708 @default.
- W4387026799 hasConceptScore W4387026799C108583219 @default.
- W4387026799 hasConceptScore W4387026799C119666444 @default.
- W4387026799 hasConceptScore W4387026799C121332964 @default.
- W4387026799 hasConceptScore W4387026799C127313418 @default.
- W4387026799 hasConceptScore W4387026799C127413603 @default.
- W4387026799 hasConceptScore W4387026799C146849305 @default.
- W4387026799 hasConceptScore W4387026799C146978453 @default.
- W4387026799 hasConceptScore W4387026799C154945302 @default.
- W4387026799 hasConceptScore W4387026799C173163844 @default.
- W4387026799 hasConceptScore W4387026799C19269812 @default.
- W4387026799 hasConceptScore W4387026799C205372480 @default.
- W4387026799 hasConceptScore W4387026799C205649164 @default.
- W4387026799 hasConceptScore W4387026799C2778102629 @default.
- W4387026799 hasConceptScore W4387026799C2778755073 @default.
- W4387026799 hasConceptScore W4387026799C41008148 @default.
- W4387026799 hasConceptScore W4387026799C58640448 @default.
- W4387026799 hasConceptScore W4387026799C62520636 @default.
- W4387026799 hasConceptScore W4387026799C62649853 @default.
- W4387026799 hasConceptScore W4387026799C81363708 @default.
- W4387026799 hasLocation W43870267991 @default.
- W4387026799 hasOpenAccess W4387026799 @default.
- W4387026799 hasPrimaryLocation W43870267991 @default.
- W4387026799 hasRelatedWork W1599766414 @default.
- W4387026799 hasRelatedWork W2019714000 @default.
- W4387026799 hasRelatedWork W2054087368 @default.
- W4387026799 hasRelatedWork W2057962381 @default.
- W4387026799 hasRelatedWork W2150101981 @default.
- W4387026799 hasRelatedWork W2463575312 @default.
- W4387026799 hasRelatedWork W2783076849 @default.
- W4387026799 hasRelatedWork W2995272598 @default.
- W4387026799 hasRelatedWork W4220885573 @default.
- W4387026799 hasRelatedWork W4243042251 @default.
- W4387026799 hasVolume "626" @default.
- W4387026799 isParatext "false" @default.
- W4387026799 isRetracted "false" @default.
- W4387026799 workType "article" @default.