Reconstruction and Efficient Visualization of Heterogeneous 3D City Models

Synergistic application of digital outcrop characterization techniques and deep learning algorithms in geological exploration

In order to meet the needs of geologists for the analysis of data characterizing field outcrops (rock sections or formations exposed on the ground surface), this study developed a field digital outcrop visualization platform based on Cesium (a 3D geospatial visualization technology) digital outcrop characterization technology. The platform was developed based on WebGL (a protocol for rendering interactions on web pages), which overcame the shortcomings of traditional software in terms of visualization, cross-device, cross-platform, and ease of use. Firstly, UAV inclined photography is used for data collection, which transforms a large amount of geological data into an intuitive 3D geological model, while the visualization platform provides rich measurement and mapping tools for the identified features, which more intuitively displays the outcrop information, helps geological explorers to understand the geological conditions in the field more quickly and comprehensively, and improves the analysis efficiency and ease-of-use of outcrop characterization data. Combined with the improved VGG19 (a deep convolutional neural network architecture) algorithm model, it has excellent performance in dealing with the fine texture and complex structure of rocks, which significantly improves the accuracy of lithology identification. The synergistic application of this technology provides geologists with a faster and more comprehensive means to understand the geological conditions in the field. The reliability of combining the Cesium digital outcrop characterization technology with the VGG19 lithology identification algorithm in geological exploration is verified through case studies. The synergistic application of this technology will greatly enhance the efficiency and ease of analysis of outcrop characterization in the field, and provide new perspectives for future research in geosciences.

Utilizing multi-criteria decision-making analysis and 3D visualization techniques for dam site selection and irrigation area identification in Gedeb River, Ethiopia

Irrigation dams and irrigation suitability analysis is important for optimal water management, crop selection and productivity, water conservation, environmental sustainability, and economic viability in agriculture arena. Thus, the main objectives of this study were to identify a suitable dam site and irrigation area in the Gedeb River, Ethiopia, using Multi-Criteria Decision-Making analysis and 3D Visualization techniques. To identify a suitable dam site, various parametrs such as rainfall, runoff, stream flow, mineral site, faulting areas, landslide site, rock types, elevation points, relief features, soil types were used while to identify a suitable irrigation area, different parametrs such as altitude, slope, soil, geological structure, distance, and land use land cover datasets were used. The necessary dataset which were used to identify a suitable dam site and irrigation area collected from Ethiopian Mapping Authority (EMA), Ethiopian irrigation and energy ministry freely. In addition, for the final irrigation dam site selection and suitable irrigation area in the Gedeb watershed, multi-criteria decision-making method with expert judgment were applied respectively. Based on the study's findings, a suitable irrigation water reservoir dam covering an area of 1886 ha, with a potential water holding capacity of 2,961,145,697 cubic meters was identified. The results also revealed a highly suitable area of 18,362.05 ha, a moderately suitable area of 19,204.05 ha, a marginally suitable area of 2095.25 ha, and a not suitable area of 2.89 ha for the aforementioned purpose. The methodological approach and research findings presented in this study can greatly assist government and non-governmental organization planners and decision-makers in the development of irrigation projects.

Seamless Navigation, 3D Reconstruction, Thermographic and Semantic Mapping for Building Inspection

We present a workflow for seamless real-time navigation and 3D thermal mapping in combined indoor and outdoor environments in a global reference frame. The automated workflow and partly real-time capabilities are of special interest for inspection tasks and also for other time-critical applications. We use a hand-held integrated positioning system (IPS), which is a real-time capable visual-aided inertial navigation technology, and augment it with an additional passive thermal infrared camera and global referencing capabilities. The global reference is realized through surveyed optical markers (AprilTags). Due to the sensor data’s fusion of the stereo camera and the thermal images, the resulting georeferenced 3D point cloud is enriched with thermal intensity values. A challenging calibration approach is used to geometrically calibrate and pixel-co-register the trifocal camera system. By fusing the terrestrial dataset with additional geographic information from an unmanned aerial vehicle, we gain a complete building hull point cloud and automatically reconstruct a semantic 3D model. A single-family house with surroundings in the village of Morschenich near the city of Jülich (German federal state North Rhine-Westphalia) was used as a test site to demonstrate our workflow. The presented work is a step towards automated building information modeling.

Fourfold Bounce Scattering-Based Reconstruction of Building Backs Using Airborne Array TomoSAR Point Clouds

Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional single-bounce scattering-based methods. It comes to our attention that the higher-order scattering points in airborne array TomoSAR point clouds may provide rich information on the backs of buildings. In this paper, the fourfold bounce (FB) scattering model of combined buildings in airborne array TomoSAR is derived, which not only explains the cause of FB scattering but also gives the distribution pattern of FB scattering points. Furthermore, a novel FB scattering-based method for the reconstruction of building backs is proposed. First, a two-step geometric constraint is used to detect the candidate FB scattering points. Subsequently, the FB scattering points are further detected by seed point selection and density estimation in the radar coordinate system. Finally, the backs of buildings can be reconstructed using the footprint inverted from the FB scattering points and the height information of the illuminated facades. To verify the FB scattering model and the effectiveness of the proposed method, the results from the simulated point clouds and the real airborne array TomoSAR point clouds are presented. Compared with the traditional roof point-based methods, the outstanding advantage of the proposed method is that it allows for the high-precision reconstruction of building backs, even in the case of poor roof points. Moreover, this paper may provide a novel perspective for the three-dimensional (3D) reconstruction of dense urban areas.

Noise-tolerant Bessel-beam single-photon imaging in fog

Reliable laser imaging is crucial to the autonomous driving. In unfavorable weather condition, however, it always suffers from the acute background noise and signal attenuation due to the harmful strong scattering. We demonstrate a noise-tolerant LiDAR with the help of Bessel beam illumination and single-photon detection. After a 31.5-m propagation in thick fog, the Bessel beam employed by our noise-tolerant LiDAR still owns a central spot with the diameter of 1.86 mm, which supports a receiving field of view as small as 60 µrad and a great suppression of the background noise. This noise-tolerant LiDAR simultaneously performs well both in depth and intensity imaging in unfavorable weather, which can be functioned as a reliable imaging sensor in automatic driving.

An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling

More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers and architects. The digital spatial reconstruction of lost historical environments is more complex, expensive and rarely done. Three-dimensional co-creative digital drawing with citizens’ collaboration could be a solution. In 2016, the City of Ghent (Belgium) launched the “3D city game Ghent” project with time as one of the topics, focusing on the reconstruction of disappeared environments. Ghent inhabitants modelled in open-source 3D software and added animated 3D gamification and Transmedia Storytelling, resulting in a 4D web environment and VR/AR/XR applications. This study analyses this low-cost interdisciplinary 3D co-creative process and offers a framework to enable other cities and municipalities to realise a parallel virtual universe (an animated digital twin bringing the past to life). The result of this co-creation is the start of an “Animated Spatial Time Machine” (AniSTMa), a term that was, to the best of our knowledge, never used before. This research ultimately introduces a conceptual 4D space–time diagram with a relation between the current physical situation and a growing number of 3D animated models over time.

Deep Neural Networks for Road Sign Detection and Embedded Modeling Using Oblique Aerial Images

Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.

Evaluating Feature Extraction Methods with Synthetic Noise Patterns for Image-Based Modelling of Texture-Less Objects

Image-based three-dimensional (3D) reconstruction is a process of extracting 3D information from an object or entire scene while using low-cost vision sensors. A structure-from-motion coupled with multi-view stereo (SFM-MVS) pipeline is a widely used technique that allows 3D reconstruction from a collection of unordered images. The SFM-MVS pipeline typically comprises different processing steps, including feature extraction and feature matching, which provide the basis for automatic 3D reconstruction. However, surfaces with poor visual texture (repetitive, monotone, etc.) challenge the feature extraction and matching stage and affect the quality of reconstruction. The projection of image patterns while using a video projector during the image acquisition process is a well-known technique that has been shown to be successful for such surfaces. In this study, we evaluate the performance of different feature extraction methods on texture-less surfaces with the application of synthetically generated noise patterns (images). Seven state-of-the-art feature extraction methods (HARRIS, Shi-Tomasi, MSER, SIFT, SURF, KAZE, and BRISK) are evaluated on problematic surfaces in two experimental phases. In the first phase, the 3D reconstruction of real and virtual planar surfaces evaluates image patterns while using all feature extraction methods, where the patterns with uniform histograms have the most suitable morphological features. The best performing pattern from Phase One is used in Phase Two experiments in order to recreate a polygonal model of a 3D printed object using all of the feature extraction methods. The KAZE algorithm achieved the lowest standard deviation and mean distance values of 0.0635 mm and −0.00921 mm, respectively.

Virtual Reality System and Scientific Visualisation for Smart Designing and Evaluating of Lighting

The current lighting solutions, both in terms of design process and later implementation, are becoming more and more intelligent. It mainly arises from higher opportunities to use information technology (IT) processes for these purposes. Designs cover many aspects, from physiological to including technical. The paper describes the problems faced by any designers while creating, evaluating them, and presenting the final results of their work in a visualisation form. Development of virtual reality (VR) technology and augmented reality, which is now taking place before our eyes, makes us inclined to think how to use this reality in lighting technology. The article presents some examples of applying VR technology in various types of smart lighting designs, for interiors and outdoor objects. The performed computer simulations are compared to reality. Some surveys, in terms of visualization rendering, were carried out. In the article, the current capabilities and main limitations of virtual reality of lighting are discussed, as well as what can be expected in the future. The luminance analysis of the virtual reality display is carried out, which shows that this equipment can be used in lighting technology after the appropriate calibration. Moreover, an innovative lighting design system based on virtual reality is presented.

Urban Cadastral Situation in Ecuador: Analysis to Determine the Degree of Proximity of the Cadastral Systems to the 3D Cadastral Model

As 3D cadastres offer advantages in several areas by providing information with greater accuracy and a high level of detail, a diagnosis of the cadastral situation is required prior to the implementation of a 3D cadastral model. Therefore, this study focuses on diagnosing the urban cadastral situation in Ecuador based on an analysis of eight cantonal decentralized autonomous governments that were selected primarily for the availability of their cadastral information. The twelve characteristics included in the analysis supported the definition of a cadastral development scale based on the fulfillment of each characteristic. The official cadastral databases, meetings, and interviews with personnel related to the cadastres were used in the analysis to gain in-depth knowledge of the situation in each canton. The findings demonstrated that most cantons had similar characteristics and are at an intermediate level of cadastral development. Therefore, there is the need for cantons to have standardized cadastral information in accordance with national and international regulations. Thus, in this research, we developed an initial Ecuadorian land administration domain model country profile to initiate the transition towards 3D cadastre.