Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps
In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation,...
Guardado en:
Autores principales: | Ingmar Nitze, Konrad Heidler, Sophia Barth, Guido Grosse |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b13024976aac4c3ebe8ec61149dc2bb5 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data
por: Ji Won Suh, et al.
Publicado: (2021) -
Automated Quantification of Brittle Stars in Seabed Imagery Using Computer Vision Techniques
por: Kazimieras Buškus, et al.
Publicado: (2021) -
Evaluation of semi-supervised learning using sparse labeling to segment cell nuclei
por: Bruch Roman, et al.
Publicado: (2020) -
A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery
por: Saüc Abadal, et al.
Publicado: (2021) -
A Deep Learning Method for Fully Automatic Stomatal Morphometry and Maximal Conductance Estimation
por: Jonathon A. Gibbs, et al.
Publicado: (2021)