Landslide extraction using a novel empirical method and binary semantic segmentation U-NET framework using sentinel-2 imagery

• Published in: Remote sensing letters Vol. 15; no. 3; pp. 326 - 338
• Main Authors: Devara, Meghanadh, Maurya, Vipin Kumar, Dwivedi, Ramji
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.03.2024; Taylor & Francis Ltd
• Subjects: Algorithms; Artificial intelligence; Availability; data collection; Datasets; Deep learning; empirical research; Feature extraction; Image processing; Image segmentation; inventories; Land cover; Landslides; Machine learning; Optical disks; Performance evaluation; remote sensing; Satellite imagery; Semantic segmentation; Semantics; topography; Training

Artificial intelligence (AI) has achieved a remarkable place in solving complex problems in almost all disciplines. Based on the recent notable performances of machine learning and deep learning techniques for rapid and automatic landslide identifcation, it is observed that availability of quality t...