We introduce a novel method for updating 3D geospatial models, specifically targeting occlusion removal in large-scale maritime environments. Traditional 3D reconstruction techniques often face problems with dynamic objects, like cars or vessels, that obscure the true environment, leading to inaccurate models or requiring extensive manual editing. Our approach leverages deep learning techniques, including instance segmentation and generative inpainting, to directly modify both the texture and geometry of 3D meshes without the need for costly reprocessing. By selectively targeting occluding objects and preserving static elements, the method enhances both geometric and visual accuracy. This approach not only preserves structural and textural details of map data but also maintains compatibility with current geospatial standards, ensuring robust performance across diverse datasets. The results demonstrate significant improvements in 3D model fidelity, making this method highly applicable for maritime situational awareness and the dynamic display of auxiliary information.
Maritime surveillance relies on advanced technologies to ensure the safety and security of national and international waters, particularly in monitoring vessel activities. Distributed Acoustic Sensing (DAS) has emerged as a powerful technology for detecting and analyzing underwater acoustic signatures along fiber-optic cables. However, the lack of annotated DAS datasets in maritime contexts, combined with the high dimensionality and unstructured nature of recorded data streams, hinders the deployment of automated solutions that rely on labeled data for vessel detection. This work introduces DASBoot, a novel annotation toolkit designed to enhance maritime surveillance by aligning vessel signatures from DAS data with Automatic Identification System (AIS) messages. Our approach integrates data processing, fusion, and visualization within a cohesive workflow that significantly reduces the cognitive load on analysts while improving the accuracy of vessel identification. The experimental results demonstrate the effectiveness of our method for dataset annotation and pave the way for future advancements in DAS-based automated maritime surveillance.
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