Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation

• URL: http://arxiv.org/abs/2504.19589v1
• Date: Mon, 28 Apr 2025 08:51:54 GMT
• Title: Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation
• Authors: Daniele Rege Cambrin, Luca Colomba, Paolo Garza,
• Abstract summary: In crisis management and remote sensing, image segmentation plays a crucial role, enabling tasks like disaster response and emergency planning.<n>The problem in their development is the data scarcity and the lack of extensive benchmark datasets, limiting the capabilities of training large neural network models.<n>We propose a novel methodology, namely Magnifier, to improve segmentation performance with limited data availability.
• Score: 4.833815605196964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In crisis management and remote sensing, image segmentation plays a crucial role, enabling tasks like disaster response and emergency planning by analyzing visual data. Neural networks are able to analyze satellite acquisitions and determine which areas were affected by a catastrophic event. The problem in their development in this context is the data scarcity and the lack of extensive benchmark datasets, limiting the capabilities of training large neural network models. In this paper, we propose a novel methodology, namely Magnifier, to improve segmentation performance with limited data availability. The Magnifier methodology is applicable to any existing encoder-decoder architecture, as it extends a model by merging information at different contextual levels through a dual-encoder approach: a local and global encoder. Magnifier analyzes the input data twice using the dual-encoder approach. In particular, the local and global encoders extract information from the same input at different granularities. This allows Magnifier to extract more information than the other approaches given the same set of input images. Magnifier improves the quality of the results of +2.65% on average IoU while leading to a restrained increase in terms of the number of trainable parameters compared to the original model. We evaluated our proposed approach with state-of-the-art burned area segmentation models, demonstrating, on average, comparable or better performances in less than half of the GFLOPs.

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