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https://w3id.org/ro-id/60444fb5-f220-4e35-9339-b5c464200d5a/ http://schema.org/copyrightHolder https://ror.org/05trd4x28 https://w3id.org/ro-id/60444fb5-f220-4e35-9339-b5c464200d5a/ http://schema.org/creator mailto:nordicesmhub@gmail.com https://w3id.org/ro-id/60444fb5-f220-4e35-9339-b5c464200d5a/ http://schema.org/dateCreated 2022-03-22 16:18:11.139639+00:00 https://w3id.org/ro-id/60444fb5-f220-4e35-9339-b5c464200d5a/ http://schema.org/dateModified 2025-03-05 01:01:23.170074+00:00 https://w3id.org/ro-id/60444fb5-f220-4e35-9339-b5c464200d5a/ http://schema.org/datePublished 2022-03-22 16:18:11.139639+00:00 https://w3id.org/ro-id/60444fb5-f220-4e35-9339-b5c464200d5a/ http://schema.org/description MASSIVE project aims at i) vastly expanding the applicability of surface mass balance regression methods by exploiting the latest machine learning techniques such as deep learning for image classification and regression. ii) Harvesting unprecedented amounts of freely available archive remote sensing data to build data cubes of well-selected glacierized regions in the world. The data cubes will contain information on snow cover area, glacier facies, albedo, glacier surface elevation changes and other relevant mass balance predictors. ​ iii) designing novel glacier facies and snow cover classification algorithms based on ML. ​ iv) Adapting the most promising network architectures for pixel-wise mapping in RS, Fully Convolutional Networks. These Networks can be trained with multi-resolution and multi-sensor data in one single classification framework. 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