ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Cryospheric Sciences
Volume 13 - 2025 | doi: 10.3389/feart.2025.1572982
GEOCLASS-image -A Versatile Machine Learning Environment for Ice-Surface Classification from High-Resolution Image Data
Provisionally accepted
- University of Colorado Boulder, Boulder, United States
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GEOCLASS-image is an open source cyberinfrastructure (CI) for automated classification of spatial surface structures based on high-resolution image data, consisting of a data-driven and physically informed neural network (NN) system and a data analysis tool for, currently, submeter resolution satellite image data (Maxar WorldView data). The objective of this paper is to introduce GEOCLASS-image v2.0, which provides a solution for two important problems in machine learning in the geosciences: (1) Version 2.0 presents an approach for creating, exporting and sharing labeled training datasets for cryospheric classification tasks for which such datasets do not currently exist. GEOCLASS-image (v2.0) offers options for user-friendly, system-immanent application using a graphical user interface (GUI), and additionally for importing and exporting data sets to facilitate interoperability with other software, a key for advancing Open Science. (2)Combining the advantages of a purely data-driven convolutional NN and a physically driven NN, a new combined NN architecture, termed VarioNet, is derived using a weighted fusion approach that includes one or several additional blocks. The GEOCLASS-image CI, demonstrated here for classification of 11 different glacier surface types which include crevasse classes and water-based classes, extracted from Maxar WorldView1 and WorldView2 data, is expected to generalize to similar classification problems in other geoscience disciplines and any high-resolution satellite imagery.
Keywords: physically driven neural network, data-driven neural network, satellite remote sensing, image classification, Glaciology, deep learning, Convolutional neural network (CNN), Open Science
Received: 08 Feb 2025; Accepted: 30 Jun 2025.
Copyright: © 2025 Twickler, Herzfeld and Trantow. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ute Herzfeld, University of Colorado Boulder, Boulder, United States
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