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PurposeDigital documentation techniques of tunneling excavations at archaeological sites are becoming more common. These methods, such as photogrammetry and LiDAR (Light Detection and Ranging), are able to create precise three-dimensional models of excavations to complement traditional forms of documentation with millimeter to centimeter accuracy. However, these techniques require either expensive pieces of equipment or a long processing time that can be prohibitive during short field seasons in remote areas. This article aims to determine the effectiveness of various low-cost sensors and real-time algorithms to create digital scans of archaeological excavations.Design/methodology/approachThe authors used a class of algorithms called SLAM (Simultaneous Localization and Mapping) along with depth-sensing cameras. While these algorithms have largely improved over recent years, the accuracy of the results still depends on the scanning conditions. The authors developed a prototype of a scanning device and collected 3D data at a Maya archaeological site and refined the instrument in a system of natural caves. This article presents an analysis of the resulting 3D models to determine the effectiveness of the various sensors and algorithms employed.FindingsWhile not as accurate as commercial LiDAR systems, the prototype presented, employing a time-of-flight depth sensor and using a feature-based SLAM algorithm, is a rapid and effective way to document archaeological contexts at a fraction of the cost.Practical implicationsThe proposed system is easy to deploy, provides real-time results and would be particularly useful in salvage operations as well as in high-risk areas where cultural heritage is threatened.Originality/valueThis article compares many different low-cost scanning solutions for underground excavations, along with presenting a prototype that can be easily replicated for documentation purposes.

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There is a need for reliable underwater fish monitoring systems that can provide oceanographers and researchers with valuable data about life underwater. Most current methods rely heavily on human observation which is both error prone and costly. FishSense provides a solution that accelerates the use of depth cameras underwater, opening the door to 3D underwater imaging that is fast, accurate, cost effective, and energy efficient. FishSense is a sleek handheld underwater imaging device that captures both depth and color images. This data has been used to calculate the length of fish, which can be used to derive biomass and health. The FishSense platform has been tested through two separate deployments. The first deployment imaged a toy fish of known length and volume within a controlled testing pool. The second deployment was conducted within an 70,000 gallon aquarium tank with multiple species of fish. A Receiver Operating Characteristic (ROC) curve has been computed based on the detector's performance across all images, and the mean and standard deviation of the length measurements of the detections has been computed.