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Cross-spectral local feature matching between visual and thermal images benefits many vision tasks in low-light environments, including image-to-image fusion and camera re-localization. An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of visible–thermal matching is the availability of large-scale and high-quality annotated datasets. However, publicly available datasets are either in relative small quantity scales or have limited pose annotations due to the expensive cost of data acquisition and annotation, which severely hinders the development of this field. In this paper, we proposed a multi-view thermal–visible image dataset for large-scale cross-spectral matching. We first recovered a 3D reference model from a group of collected RGB images, in which a certain image (bridge) shares almost the same pose as the thermal query. We then effectively registered the thermal image to the model based on manually annotating a 2D-2D tie point between the bridge and the thermal. In this way, through simply annotating one same viewpoint image pair, numerous overlapping image pairs between thermal and visible could be available. We also proposed a semi-automatic approach for generating accurate supervision for training multi-view cross-spectral matching. Specifically, our dataset consists of 40,644 cross-modal pairs with well supervision, covering multiple complex scenes. In addition, we also provided the camera metadata, 3D reference model, depth map of the visible images and 6-DoF pose of all images. We extensively evaluated the performance of state-of-the-art algorithms on our dataset and provided a comprehensive analysis of the results. We will publish our dataset and pre-processing code. [ABSTRACT FROM AUTHOR]

• Propose a deep-learning intelligent system incorporating data augmentation for STVS assessment. • Build a BiGRU-attention-based assessment model to extract temporal dependencies. • Effectiveness and superiority of the approach is verified by the New England 39-bus system. • Statistical tests have been performed to examine the performance of the proposal. Facing the difficulty of expensive and trivial data collection and annotation, how to make a deep learning-based short-term voltage stability assessment (STVSA) model work well on a small training dataset is a challenging and urgent problem. Although a big enough dataset can be directly generated by contingency simulation, this data generation process is usually cumbersome and inefficient; while data augmentation provides a low-cost and efficient way to artificially inflate the representative and diversified training datasets with label preserving transformations. In this respect, this paper proposes a novel deep-learning intelligent system incorporating data augmentation for STVSA of power systems. First, due to the unavailability of reliable quantitative criteria to judge the stability status for a specific power system, semi-supervised cluster learning is leveraged to obtain labeled samples in an original small dataset. Second, to make deep learning applicable to the small dataset, conditional least squares generative adversarial networks (LSGAN)-based data augmentation is introduced to expand the original dataset via artificially creating additional valid samples. Third, to extract temporal dependencies from the post-disturbance dynamic trajectories of a system, a bi-directional gated recurrent unit with attention mechanism based assessment model is established, which bi-directionally learns the significant time dependencies and automatically allocates attention weights. The test results demonstrate the presented approach manages to achieve better accuracy and a faster response time with original small datasets. Besides classification accuracy, this work employs statistical measures to comprehensively examine the performance of the proposal. [ABSTRACT FROM AUTHOR]