Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images

• URL: http://arxiv.org/abs/2507.04915v1
• Date: Mon, 07 Jul 2025 12:02:35 GMT
• Title: Leveraging Self-Supervised Features for Efficient Flooded Region Identification in UAV Aerial Images
• Authors: Dibyabha Deb, Ujjwal Verma,
• Abstract summary: This work focuses on leveraging self-supervised features to accurately identify flooded regions in UAV aerial images.<n>We evaluate the effectiveness of features from the DINOv2 model, trained on non-aerial images, for segmenting aerial images.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying regions affected by disasters is a vital step in effectively managing and planning relief and rescue efforts. Unlike the traditional approaches of manually assessing post-disaster damage, analyzing images of Unmanned Aerial Vehicles (UAVs) offers an objective and reliable way to assess the damage. In the past, segmentation techniques have been adopted to identify post-flood damage in UAV aerial images. However, most of these supervised learning approaches rely on manually annotated datasets. Indeed, annotating images is a time-consuming and error-prone task that requires domain expertise. This work focuses on leveraging self-supervised features to accurately identify flooded regions in UAV aerial images. This work proposes two encoder-decoder-based segmentation approaches, which integrate the visual features learned from DINOv2 with the traditional encoder backbone. This study investigates the generalization of self-supervised features for UAV aerial images. Specifically, we evaluate the effectiveness of features from the DINOv2 model, trained on non-aerial images, for segmenting aerial images, noting the distinct perspectives between the two image types. Our results demonstrate that DINOv2's self-supervised pretraining on natural images generates transferable, general-purpose visual features that streamline the development of aerial segmentation workflows. By leveraging these features as a foundation, we significantly reduce reliance on labor-intensive manual annotation processes, enabling high-accuracy segmentation with limited labeled aerial data.

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