A major shortfall of vision‐based inspection solutions is the lack of scale information, required to resolve inspection regions to a physical scale. To address this challenge, a learning‐based scale estimation technique is proposed. The underlying assumption is that the surface texture of structures, captured in images, contains enough information to estimate scale for each corresponding image (e.g., pixel/mm). This permits the training of a regression model to establish the relationship between surface textures, captured in images, and scales. A convolutional neural network is trained to extract scale‐related features from textures captured in images. Then, the trained model can be exploited to estimate scales for all images that are captured from a structure's surfaces with similar textures. The capability of the proposed technique is demonstrated using data collected from surface textures of three different structures. An average scale estimation error, from images of each structure, is less than 15%.

Reconnaissance teams collect perishable data after each disaster to learn about building performance. However, often these large image sets are not adequately curated, nor do they have sufficient metadata (e.g., GPS), hindering any chance to identify images from the same building when collected by different reconnaissance teams. In this study, Siamese convolutional neural networks (S-CNN) are implemented and repurposed to establish a building search capability suitable for post-disaster imagery. This method can automatically rank and retrieve corresponding building images in response to a single query using an image. In the demonstration, we utilize real-world images collected from 174 reinforcedconcrete buildings affected by the 2016 Southern Taiwan and the 2017 Pohang (South Korea) earthquake events. A quantitative performance evaluation is conducted by examining two metrics introduced for this application: Similarity Score (SS) and Similarity Rank (SR).

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