Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.

Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery / Galatola, Marco; Arnaudo, Edoardo; Barco, Luca; Rossi, Claudio; Dominici, Fabrizio. - ELETTRONICO. - (2023), pp. 6952-6955. (Intervento presentato al convegno 2023 IEEE International Symposium on Geoscience and Remote Sensing (IGARSS 2023) tenutosi a Pasadena (USA) nel 16 - 21 July, 2023) [10.1109/IGARSS52108.2023.10281933].

Land Cover Segmentation with Sparse Annotations from Sentinel-2 Imagery

Arnaudo, Edoardo;Barco, Luca;Dominici, Fabrizio
2023

Abstract

Land cover (LC) segmentation plays a critical role in various applications, including environmental analysis and natural disaster management. However, generating accurate LC maps is a complex and time-consuming task that requires the expertise of multiple annotators and regular updates to account for environmental changes. In this work, we introduce SPADA, a framework for fuel map delineation that addresses the challenges associated with LC segmentation using sparse annotations and domain adaptation techniques for semantic segmentation. Performance evaluations using reliable ground truths, such as LUCAS and Urban Atlas, demonstrate the technique's effectiveness. SPADA outperforms state-of-the-art semantic segmentation approaches as well as third-party products, achieving a mean Intersection over Union (IoU) score of 42.86 and an F1 score of 67.93 on Urban Atlas and LUCAS, respectively.
2023
979-8-3503-2010-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2981337