<p><strong>Abstract.</strong> This paper provides an overview of state-of-the-art technology and sensor for the inventory of industrial archaeology. As an object of study, a historical copper shaft the Quincy Mine in Michigan State Upper Peninsula was chosen. This shaft was operated for nearly 100 years from 1846 to 1945 and today, what remains of the shaft is a part of the Keweenaw National Historical Park. The main sensor for data acquisition that was used is a 3D camera Matterport. In given research, the results of the above ground scanning using of Matterport are presented. Before scanning a calibration of Matterport camera was performed. The calibration was done by comparison of coordinates of targets determined by FARO Scanner. It was found out that the camera has significant systematic errors which have to be accounted during data processing. Because of the specific data structure of Matterport camera, only the scale factor was calculated and then applied to point clouds. Field works comprised historical and archive data collection and analysis, reconnaissance and scanning of the Quincy Mine interior. As a final result of the research 3D model of the Quincy Mine interior was constructed. Further, this model is going to be used for the Quincy Mine Museum virtual tours. There are many advantages to using Matterport. First of all, such a technology does not need dense geodetic support, average working time at station equals to 2–3 minutes. Cost of Matteroprt equipment is competitive to the cheapest terrestrial laser scanners.</p>

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In spite of the tremendous success in artificial intelligence technology and a high level of automation in geospatial data obtaining processes, there is still a need for topographical field data collection by professional surveyors. Understanding terrain topology and topography is a cognitive skill set that has to be demonstrated by geospatial Subject Matter Experts (SME) for the productive work in the topographic surveying field. For training of the mentioned above skillset, one has to be exposed to the theory and must also practice with surveying instruments in field conditions. The challenge of any surveying/geospatial engineering workforce training is to expose students to field conditions which might be limited due to equipment expenses and meteorological conditions that prevent good data collection. To meet this challenge, the Integrated Geospatial Technology research group is working on a geospatial virtual reality (VR) project which encompasses the following components: (a) immersive visualization of terrain; (b) virtual total station instrument; (c) virtual surveyor with reflector installed on the virtual rod. The application scenario of the technology we are working with has the following stages: (1) student is installing total station on the optimal location; (2) students move virtual surveyor on the sampling points they consider to be important (3) contours are generated and displayed in 3D being superimposed on 3D terrain; (4) accuracy of terrain modeling is observable and measurable by comparing the sampling model with initial one.

Protecting the future of forests in the United States and other countries depends in part on our ability to monitor and map forest health conditions in a timely fashion to facilitate management of emerging threats and disturbances over a multitude of spatial scales. Remote sensing data and technologies have contributed to our ability to meet these needs, but existing methods relying on supervised classification are often limited to specific areas by the availability of imagery or training data, as well as model transferability. Scaling up and operationalizing these methods for general broadscale monitoring and mapping may be promoted by using simple models that are easily trained and projected across space and time with widely available imagery. Here, we describe a new model that classifies high resolution (~1 m2) 3-band red, green, blue (RGB) imagery from a single point in time into one of four color classes corresponding to tree crown condition or health: green healthy crowns, red damaged or dying crowns, gray damaged or dead crowns, and shadowed crowns where the condition status is unknown. These Tree Crown Health (TCH) models trained on data from the United States (US) Department of Agriculture, National Agriculture Imagery Program (NAIP), for all 48 States in the contiguous US and spanning years 2012 to 2019, exhibited high measures of model performance and transferability when evaluated using randomly withheld testing data (n = 122 NAIP state x year combinations; median overall accuracy 0.89–0.90; median Kappa 0.85–0.86). We present examples of how TCH models can detect and map individual tree mortality resulting from a variety of nationally significant native and invasive forest insects and diseases in the US. We conclude with discussion of opportunities and challenges for extending and implementing TCH models in support of broadscale monitoring and mapping of forest health.

An ultrasonic Positioning System (UPS) has outperformed RF-based systems in terms of its accuracy for years. However, few of the developed solutions have been deployed in practice to satisfy the localization demand of today's smart devices, which lack ultrasonic sensors and were considered as being "deaf" to ultrasound. A recent finding demonstrates that ultrasound may be audible to the smart devices under certain conditions due to their microphone's nonlinearity. Inspired by this insight, this work revisits the ultrasonic positioning technique and builds a practical UPS, called UPS+, for ultrasound-incapable smart devices. The core concept is to deploy two types of indoor beacon devices, which will advertise ultrasonic beacons at two different ultrasonic frequencies respectively. Their superimposed beacons are shifted to a low-frequency by virtue of the nonlinearity effect at the receiver's microphone. This underlying property functions as an implicit ultrasonic downconverter without throwing harm to the hearing system of humans. We demonstrate UPS+, a fully functional UPS prototype, with centimeter-level localization accuracy using custom-made beacon hardware and well-designed algorithms.