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Semantic segmentation as a part of geological mapping using artificially blended texture dataset

Geological mapping is essential for understanding the Earth's surface and subsurface structures, aiding resource exploration, environmental monitoring, and hazard assessment. Semantic segmentation, a computer vision technique, has shown promise in automating geological mapping processes by classifying image pixels into meaningful categories. This study explores the integration of semantic segmentation into geological mapping workflows by leveraging an artificially blended texture dataset.

Traditional geological mapping relies on extensive fieldwork in combination with manual aerial or satellite imagery interpretation, which can be time-consuming and subjective. Semantic segmentation can efficiently classify geological features by learning distinctive patterns and textures from data. However, obtaining high-quality datasets for this purpose is challenging due to the heterogeneous nature of geological formations and limited ground truth data.

We address this challenge by employing an artificially blended texture dataset that combines real-world geological textures. This blended dataset aims to enrich the training data with diverse texture and geological feature combinations, potentially enhancing the model's ability to generalize to unseen terrain conditions. Moreover, it reduces the potential for label bias by eliminating the need for manual delineation of label classes in the image, instead relying on generated borders.

Through experimental evaluation, we explore the effectiveness of semantic segmentation with the blended texture dataset in accurately delineating geological units and structures. We also discuss the implications of incorporating semantic segmentation into geological mapping workflows at the Baltic cliff coast, including its potential for improving mapping efficiency, reducing human bias, and facilitating remote sensing data integration with geological interpretations.

Details

Author
Jewgenij Torizin1, Nick Schüßler1, Michael Fuchs1, Dirk Kuhn1, Karsten Schütze2, Steffen Prüfer1, Claudia Gunkel1
Institutionen
1Bundesanstalt für Geowissenschaften und Rohstoffe, Germany; 2Landesamt für Umwelt, Naturschutz und Geologie, Mecklenburg-Vorpommern, Germany
Veranstaltung
GeoSaxonia 2024
Datum
2024
DOI
10.48380/pz2x-6229