The paper aims to study the geometrical quality and opportunities of the state-of-the-art 3D camera Matterport Pro and examine its potential for industrial archaeology applications. The presented study consisted of two steps. In the first step, the geometrical quality of the camera-generated point cloud was investigated on the calibration test field. The geometrical quality was checked in two ways: (1) with distance comparison between reference targets and (2) with point cloud comparison. The coordinates of the reference targets were determined using a high-precision total station, while the FARO Scanner generated the reference point cloud. The study established that Matterport Pro has a scale systematic error that must be accounted for in 3D modeling and the inventory of archaeological objects. In the second step, the geometrical quality of the camera was checked for the actual archaeological object. As such an object, the historical copper-shaft Quincy Mine in Michigan State Upper Peninsula was considered. The specific subject of the study was one of the largest hoist engines in the world. The Matterport Pro camera scanned the indoor environment of the hoist engine house. The accuracy of the 3D model of the hoist engine was checked using additional linear measurements on-site. It was found that the accuracy of 1% showed that the camera specification can be improved through calibration. As an output of the second step, the accurately refined 3D model of the hoist engine's interior was built. That model was embedded into a 3D model of the hoist engine's house for usage in virtual tours of the Quincy Mine Museum. Finally, a virtual tour was created of the Quincy Mine house with exterior and interior models referenced to the geographical frame.

Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable, and the coastline change can cause enormous damage to the ecology, economy, and population of the coastal part of Kazakhstan. In this work, we use remote sensing and Geographic Information System (GIS) technologies to study the changes in the coastline of the northeastern Caspian Sea and predict the extent of flooding with increasing water levels. The proposed methodology for creating dynamic maps can be used to monitor the coastline and forecast the extent of flooding in the area. As a result of this work, the main factors affecting changes in the coastline were identified. After analyzing the water level data from 1988 to 2019, it was revealed that the rise in water level was observed from 1980 to 1995. The maximum sea level rise was recorded at −26.04 m. After that, the sea level began to fall, and between 1996 and 2009, there were no significant changes; the water level fluctuated with an average of −27.18 m. Then, a map of the water level dynamics in the Caspian Sea from 1988 to 2019 was compiled. According to the dynamics map, water level rise and significant coastal retreat were revealed, especially in the northern part of the Caspian Sea and the northern and southern parts of Sora Kaydak. The method for predicting the estimated flooding area was described. As a result, based on a single map, the flooding area of the northeast coast was predicted. A comparative analysis of Landsat and SRTM data is presented.

To select an optimal and environmentally friendly technology for oil and gas development, it is necessary to estimate in advance the likely disfigurement processes of the surface terrain. To this end, it is recommended to develop predictive geodynamic models prior to start of field development, taking into consideration the geological characteristics and tectonic activity of the area under investigation, as well as the specific features of the reservoir. Research methods. In this paper, two models of subsidence of the ground surface in a hydrocarbon field are considered: a parametric spatial model developed at Delft University of Technology and a model based on the Knoté influence function developed at the Canadian Center for Geodetic Engineering. The first method is more suitable for describing a smooth and gradual subsidence in deep gas reservoirs and allows you to assess the spatial-temporal pattern of movement of the ground surface. In the second method, geodynamic processes are modeled based on the functional relationship between reservoir compaction and subsidence of the day surface, taking into account the location of the oil reservoir, physical and mechanical properties of rocks, changes in reservoir pressure and the results of surface disfigurement monitoring and is recommended for oil fields. Research results. A comparative analysis of these methods is carried out on the example of the Tengiz oil and gas field in Western Kazakhstan. An evaluation of the developed model accuracy is carried out by comparing the calculated values ​​of soil subsidence with the data of radar interferometry, and estimates obtained by other researchers. Recommendations are given on the application of the considered methods in the generation of predictive models of oil and gas fields, the necessity of calculating the transfer coefficient of the reservoir compaction to the position of the day surface, taking into account the depth of the reservoir and the physical and mechanical properties of the rock massif, is indicated.

Information about copper deposits of Kazakhstan, development of which is carried out in the Saryarka region, and its role in the development of the mining industry are considered in the article. Geological, structural and tectonic features of the deposits are presented. Research results on improvement methods of studying and geomechanical processes management in the development of mineral resources are presented. It is shown that the problem of geomechanical processes management can be solved on the basis of a methodology for rock condition geomonitoring considered in this article, which provides comprehensive accounting and analysis of all-natural and technogenic factors, as well as use of control tools developed by the authors. The article presents technical solutions to ensure operational safety during the development of Saryarka region field reserves, which occur in difficult mining and geological conditions. Ore bodies of the deposit have different sizes and are located at different depths, therefore, seismic surveys are carried out. The geodetic network of provisional seismic surveys at the field has been substantiated. It is proposed to conduct surveys using modern geodetic instruments, such as satellite technologies, electronic, digital geodetic instruments. The geodetic survey methods proposed by the authors provide information on the bowels of the earth with a high degree of accuracy.

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.

Given the limited number of human GIS/image analysts at any organization, use of their time and organizational resources is important, especially in light of Big Data application scenarios when organizations may be overwhelmed with vast amounts of geospatial data. The current manuscript is devoted to the description of experimental research outlining the concept of Human-Computer Symbiosis where computers perform tasks, such as classification on a large image dataset, and, in sequence, humans perform analysis with Brain-Computer Interfaces (BCIs) to classify those images that machine learning had difficulty with. The addition of the BCI analysis is to utilize the brain’s ability to better answer questions like: “Is the object in this image the object being sought?” In order to determine feasibility of such a system, a supervised multi-layer convolutional neural network (CNN) was trained to detect the difference between ‘ships’ and ‘no ships’ from satellite imagery data. A prediction layer was then added to the trained model to output the probability that a given image was within each of those two classifications. If the probabilities were within one standard deviation of the mean of a gaussian distribution centered at 0.5, they would be stored in a separate dataset for Rapid Serial Visual Presentations (RSVP), implemented with PsyhoPy, to a human analyst using a low cost EMOTIV “Insight” EEG BCI headset. During the RSVP phase, hundreds of images per minute can be sequentially demonstrated. At such a pace, human analysts are not capable of making any conscious decisions about what is in each image; however, the subliminal “aha-moment” still can be detected by the headset. The discovery of these moments are parsed out by exposition of Event Related Potentials (ERPs), specifically the P300 ERPs. If a P300 ERP is generated for detection of a ship, then the relevant image would be moved to its rightful designation dataset; otherwise, if the image classification is still unclear, it is set aside for another RSVP iteration where the time afforded to the analyst for observation of each image is increased each time. If classification is still uncertain after a respectable amount of RSVP iterations, the images in question would be located within the grid matrix of its larger image scene. The adjacent images to those of interest on the grid would then be added to the presentation to give an analyst more contextual information via the expanded field of view. If classification is still uncertain, one final expansion of the field of view is afforded. Lastly, if somehow the classification of the image is indeterminable, the image is stored in an archive dataset.

Abstract. 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.

This paper represents the overview of hydrographic surveying and different types of modern and traditional surveying equipment, and data acquisition using the traditional single beam sonar system and a modern fully autonomous underwater vehicle (AUV) IVER3. During the study, the data sets were collected using the vehicles of the Great Lake Research Center at Michigan Technological University. This paper presents how to process and edit the bathymetric data on SonarWiz5. Lastly, it compares the accuracy of the two different sonar systems in the different missions and creates 3D models to display and understand the elevations changes. Moreover, the 3D models were created after importing the data sets in the same coordinate system. In this study, the data sets were recorded by two different sensors in the two study locations in the Keweenaw Waterway in Michigan, U.S. between the cities of Houghton and Hancock. The first one equipment is the Lowrance HDS-7 sonar on the surveying boat, and other one is the EdgeTech 2205 sonar on the fully AUV of IVER3. One of the purposes of this study is to explore the sonar post processing programs, which are very important to interpret sonar and bathymetric data, and obtained the same coordinate system of the study areas. During the project, three main processing programs were used. The first one is UnderSee Explorer 2.6, which has been used to process the data sets of Polar SV boat. Secondly, EdgeTech Discover 4600 bathymetric software used EdgeTech 2205 sonar data sets to create bathymetric files that were used in SonarWiz5. Lastly, SonarWiz5 sonar processing software can be used to process the data sets. After the data acquisition and the data process, six profiles from the first study area and the five profiles from the second study are created to compare the data sets and elevations difference. It is shown that single beam sonar might miss some details, such as pipeline and quick elevation changes on seabed when we compare to the side scan sonar of IVER3 because the single side scan sonar can acquire better resolutions to understand the 3D features, such as pipelines, reliefs etc.

This paper provides an overview of oil spill scenarios and the remote sensing methods used for detection and mapping the spills. It also discusses the different kinds of thermal sensors used in oil spills detection. As UAS is becoming an important player in the oil and gas industry for the low operating costs involved, this research involved working with a cheap thermal airborne sensor mounted on DJI Phantom 4 system. Data were collected in two scenarios, first scenario is collecting data in Michigan’s Upper Peninsula at a petroleum company location and the second scenario was an indoor experiment simulating an offshore spill. The aim of this research is to inspect the capability of Lepton LWIR inexpensive sensor to detect the areas contaminated with oil. Data processing to create classification maps involved using ArcGIS 10.5.1, ERDAS Imagine 2015 and ENVI 5.3. Depending accuracy assessment (confusion matrices) for the classified images and comparing classified images with ground truth, results shows the Lepton thermal sensor worked well in differentiating oil from water and was not a good option when there are many objects in the area of interest. Future research recommendations and conclusions are presented.

The key point of this paper is presentation of the main idea and some results of the project “Small UAS in civil engineering application scenarios” (SUAS-CAS). This project was proposed by newly established in 2016 ISPRS WG V/7: “Innovative Technologies in Training Civil Engineers and Architects”. Here we are presenting our experience in using low-cost UAS in training architects at Kyiv National University of Construction and Architecture, which was chosen as basic for this project. In the first part of paper, the project outline is presented. Then the first and possible follow project outcomes were described. In some details is described the training module “Small UAS in architecture” which was developed and included as a part of the subject “Architectural photogrammetry”.

With the advent of GPS/GNSS satellite navigation systems and Unmanned Aerial Systems (UAS) surveying profession is nowadays facing its transformative stage. Written by a team of surveying experts, Surveyor’s Instruments and Technology gives surveying students and practitioners profound understanding of how surveying instruments are designed and operating based on surveying instrument functionality. The book includes the required basic knowledge of accurate measurements of distances and angles from theoretical principles to advanced optical, mechanical, electronic and software components for comparative analysis. Readers are presented with basic elements of UAS systems, practical interpretation techniques, sensor components, and operating platforms. Appropriate for surveying courses at all levels, this guide helps students and practitioners alike to understand what is behind the buttons of surveying instruments of all kinds when considering practical project implementations.

Abstract. Object-based approaches in the segmentation and classification of remotely sensed images yield more promising results compared to pixel-based approaches. However, the development of an object-based approach presents challenges in terms of algorithm selection and parameter tuning. Subjective methods are often used, but yield less than optimal results. Objective methods are warranted, especially for rapid deployment in time-sensitive applications, such as earthquake damage assessment. Herein, we used a systematic approach in evaluating object-based image segmentation and machine learning algorithms for the classification of earthquake damage in remotely sensed imagery. We tested a variety of algorithms and parameters on post-event aerial imagery for the 2011 earthquake in Christchurch, New Zealand. Results were compared against manually selected test cases representing different classes. In doing so, we can evaluate the effectiveness of the segmentation and classification of different classes and compare different levels of multistep image segmentations. Our classifier is compared against recent pixel-based and object-based classification studies for postevent imagery of earthquake damage. Our results show an improvement against both pixel-based and object-based methods for classifying earthquake damage in high resolution, post-event imagery.

Significant efforts are invested by rescue agencies worldwide to save human lives during natural and man-made emergency situations including those that happen in wilderness locations. These emergency situations include but not limited to: accidents with alpinists, mountainous skiers, people hiking and lost in remote areas. Sometimes in a rescue operation hundreds of first responders are involved to save a single human life. There are two critical issues where geospatial imaging can be a very useful asset in rescue operations support: 1) human detection and 2) confirming a fact that detected a human being is alive. International group of researchers from the Unites States and Poland collaborated on a pilot research project devoted to identify a feasibility of use for the human detection and alive-human state confirmation small unmanned aerial vehicles (SUAVs) and inexpensive forward looking infrared (FLIR) sensors. Equipment price for both research teams was below $8,000 including 3DR quadrotor UAV and Lepton longwave infrared (LWIR) imager which costs around $250 (for the US team); DJI Inspire 1 UAS with commercial Tamarisc-320 thermal camera (for the Polish team). Specifically both collaborating groups performed independent experiments in the USA and Poland and shared imaging data of on the ground and airborne electro-optical and FLIR sensor imaging collected. In these experiments dead bodies were emulated by use of medical training dummies. Real humans were placed nearby as live human subjects. Electro-optical imagery was used for the research in optimal human detection algorithms. Furthermore, given the fact that a dead human body after several hours has a temperature of the surrounding environment our experiments were challenged by the SUAS data optimization, i.e., distance from SUAV to object so that the FLIR sensor is still capable to distinguish temperature differences between a dummy and a real human. Our experiments indicated feasibility of use SUAVs and small thermal sensors for the human detection scenarios described above. Differences in temperatures were collected by deployed imaging acquisition platform are interpretable on FLIR images visually. Moreover, we applied ENVI image processing functions for calibration and numerical estimations of such a temperature differences. There are more potential system functionalities such as voice messages from rescue teams and even distant medication delivery for the victims of described emergencies. This paper describes experiments, processing results, and future research in more details.

The main goal of geospatial technologies in various multi-dimensional environments is to provide decision makers with relevant information at optimal time intervals. Some of the most challenging application scenarios, where visual analytics is important, are associated with efficient decision support during anthropogenic and natural environmental accidents, such as floods, hurricanes, earthquakes, oil spills, terrorist attacks, and others. The time required for processing information is critical in each scenario. Despite advances in automated geospatial image processing, the “human in the loop” is still necessary because current applications depend upon complex algorithms and adequate classification rules can only be provided by skilled geospatial professionals. Given the limited number of human GIS/image analysts at any organization deploying multi-dimensional environments, the rational and efficient use of their time is important. One of the most obvious ways to optimize image analyst’s workflow is to develop human-computer interactions method that will take less of their time to perform operations and will not interrupt their attention from primary tasks. Specifically, potentially innovative and promising solutions for the problem can be associated with applying human-centric geospatial technologies as a way to utilize human-computer symbiosis for accelerated control of the vast amounts of geospatial data processing. The ultimate goal of cognitive geospatial technology research and development is establishing an interactive geospatial environment optimizing decision support workflow, making it more efficient and accelerating productivity by producing automatic reactions to an analyst’s attention, emotions, and minds. This paper describes innovative approach and research experiments on integrating wireless wearable electroencephalography (EEG) device within geospatial technology workflow. Preliminary results indicate opportunities of design geospatial systems controlled by “power of the human mind”.

The main goal of a cyberspace environment is to support decision makers with relevant information on time for operational use. Cyberspace environments depend on geospatial data including terrestrial, aerial/UAV, satellite and other multi-sensor data obtained in electro-optical and other imaging domains. Despite advances in automated geospatial image processing, the "human in the loop" is still necessary because current applications depend upon complex algorithms and adequate classification rules that can only be provided by skilled geospatial professionals. Signals extracted from humans may become an element of a cyberspace system. This paper describes research experiments on integrating an EEG device within geospatial technology.