Constructing high quality three-dimensional (3D) geological models of coal seams from multi-source data, such as boreholes and geological maps, is a crucial task in seam distribution analysis and production planning of coal mine. However, widely used interpolation methods in geology, such as inverse distance weighting (IDW) and ordinary kriging (OK), ignore the variety of uncertainties in multi-source data. In this paper, sample points extracted from the archival data of a coal mine were classified into two categories, the hard data and the soft data, according to their sources. Field boundaries and fault observations were also obtained from data processing. A 3D geological modeling workflow was developed to integrate these data, in which (1) the weighted kriging (WK) method was used to interpolate both hard data and soft data, and (2) a fault modeling method was proposed to build faults’ geometry as well as their impact on coal seam surface model. The WK method improves the accuracy of interpolation compared with the OK and IDW methods, and the proposed fault modeling method can integrate expert interpretations into the final 3D geological model of the coal seams.

Three-dimensional (3D) modeling of geological surfaces, such as coal seams and strata horizons, from sparsely sampled data collected in the field, is a crucial task in geological modeling. Interpolation is a common approach for this task to construct continuous geological surface models. However, this problem becomes challenging considering the impact of the faults on geological surfaces. Existing methods tend to solve this problem through three steps, including interpolating stratum and fault surface, applying a fault modeling method to modify the geological surface, and optimizing the modified surface to pass sample points fallen into the fault displacement zone. This paper presents a more concise method to generate a faulted geological surface, in which 1) a constrained Delaunay triangulated irregular network (CD-TIN) is constructed to facilitate the neighborhood search process of the ordinary kriging (OK) interpolation, 2) the CD-TIN is also directly constrained by horizon cut-off lines formed from theoretical fault displacement profiles, and 3) subsequently, neighbors of the location to be estimated are selected effectively in the CD-TIN considering the fault topology. The proposed method significantly improves the time efficiency of the OK interpolation by utilizing the CD-TIN and incorporates fault effects directly into the interpolation process by inserting fault horizontal cut-off lines into CD-TIN. Moreover, by integrating the fault effects directly into the interpolation process, the surface modeling process is accomplished in a single stage instead of two separate stages of interpolation first and then modifying the surface in the fault area. By this strategy, the proposed method significantly improves the time efficiency of the OK interpolation algorithm and achieves more accurate modeling of the faulted geological surface. Experiments were designed to compare the performance of our method with several commonly used approaches, and the results indicate that the proposed TIN-constrained OK method achieves better accuracy and efficiency in modeling faulted geological surfaces than other methods. This method could also be used in geospatial interpolation studies, such as meteorological data interpolation.

Oblique photogrammetry models are indispensable for implementing digital twins of cities. Geographic information system researchers have proposed plenty of methods to load and visualize these city-scaled scenes. However, when the area viewed changes quickly in real-time rendering, current methods still require excessive GPU calculation and memory occupation. In this study, we propose a data organization method in which we merged all quadtrees and used a binary encoding method to encode nodes in a merged tree so that the parent–child relationship between the tree nodes could be calculated using rapid binary operations. After that, we developed a strategy to cancel the loading of redundant nodes based on the parent–child relationship, which helped to reduce the hard disk loading time and the amount of memory occupied in visualization. Moreover, we introduced a parameter to measure the area of the triangle mesh per pixel to achieve unified data scheduling under different production standards. We implemented our method based on Unreal Engine (UE), and three experiments were designed to illustrate the advantages of our methods in index acceleration, frame time, and memory reduction. The results show that our methods can significantly improve visualization fluency and reduce memory usage.

This paper presents a novel pipeline for visual route recognition (VRR) in large-scale urban environments, leveraging open street view data. The proposed approach aims to identify the path of a video recorder by analyzing visual cues from continuous video frames and street landmarks, evaluated through datasets from New York and Taipei City. The pipeline begins with SemVG (Semantic Visual Geo-localization), a semantic fused feature extraction network that filters out non-landmark noise, generating robust visual representations. We construct a feature database from multi-perspective street view images to enable efficient feature retrieval for query video frames. Additionally, we introduce a spatio-temporal trajectory reconstruction method that corrects mismatches in the camera's motion path, ensuring consistency. Our contributions include the development of SemVG, a method for maintaining spatio-temporal consistency in trajectory reconstruction, and a large-scale Taipei dataset designed for visual route recognition. This work has implications for urban surveillance, law enforcement, and smart city applications, supporting urban planning, resource management, search and rescue, and augmented reality navigation by improving localization without specialized hardware.