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
• DOI: 10.1080/2150704X.2024.2320178
• ISSN: 2150-704X; 2150-7058

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 training data, proper model training and associated cost are crucial for developing such frameworks. Therefore, the primary objective of the study is to propose a novel empirical algorithm, DvD, for rapid landslide identification using Sentinel-2 imagery and comparatively evaluate its performance to a deep learning architecture popularly used in feature extraction problems, binary semantic segmentation U-NET (BSS-UNET) framework. The empirical method has been investigated over a dataset diverse in topography and land cover to evaluate its efficacy. The proposed BSS-UNET framework is trained on the landslide database provided by the Institute of Advance Research in Artificial Intelligence (IARAI) in Landslide4Sense 2022 challenge which achieved a high mIoU value of 0.78 with 84.23% precision, 65% recall and 73.32 F1-score. The DvD algorithm outperformed the BSS-UNET framework and achieved 0.80 mIoU when applied to the IARAI dataset. The proposed empirical method has the potential to serve as large-scale rapid landslide inventory preparation subject to the availability of cloud-free satellite imagery.