Description
Glaciers are under pressure in the current climate warming trend. The aerial extent and the mass balance, a measure of mass loss or mass gain of a glacier are representative of the “health state” of a glacier. They are recognized as Essential Climate Variables by the World Meteorological Organization. Due to the sheer number of glaciers of roughly 200.000 worldwide and their inaccessibility, satellite data has been proven as a valuable source to map glaciers around the globe. Satellite imagery has enabled researchers to map the extent of glaciers and glacier surface types (glacier facies, i.e. snow, firn, ice). Multi-temporal information on the glacier surface elevation and mass balance proxies (snow cover, albedo) which are highly correlated with the glacier mass balance have been used to estimate mass changes. Today the ever-growing amount of satellite data motivates the development of new methodology to maximize the information retrieved (value-added) and ease the handling and storage of the data in data cubes.Recently, the field of machine learning has been revolutionized by deep learning. In the context of image analysis, Convolutional Neural Networks are among the most successful deep learning algorithms as they can learn a hierarchy of spatial features at different layers of the network associated to increasing levels of abstraction, i.e., from raw pixel values to parts of objects (edges and corners), local shapes, up to complex structures in satellite and aerial images.
In MASSIVE the most promising network architectures for pixel-wise mapping in remote sensing, Fully Convolutional Networks, will be adapted to map glacier extend and estimate glacier mass balance.
Period | 18 Jan 2023 |
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Event title | Mini Symposium on Development and Sharing of Open Geodata 2023 |
Event type | Conference |
Location | Enschede, NetherlandsShow on map |
Degree of Recognition | International |
Keywords
- CNN
- Earth observation
- Glacier mapping
- Machine learning
- Open data