3D Geographic Information System Research Articles

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing.

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a "building-floor-room" hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

From top to deep: An integrated multidisciplinary approach for the study of a transformative landscape of Savatra ancient city

AbstractIn this study, a combined workflow of computational methodologies is introduced to explore the transformative landscape of the ancient city of Savatra (Central Anatolia Region, Türkiye), which faces long‐term risks stemming from natural and anthropogenic threats. Emphasis was placed on regional and local scale landscape analysis, employing aerial and ground‐based remote‐sensing techniques to unravel past settlement patterns and understand the impact of environmental factors, topography and natural resources on both the location of Savatra and spatial organization of its features. On a regional scale, the influence of hydrological conditions, slope and aspect on the landscape was determined through the employment of Geographical Information System (GIS)‐based analysis of digital elevation models (DEMs). At a more local scale, the utilization of the Unmanned Aerial Systems‐derived DEM and geophysical survey helped identify potential archaeological features and also assessed the risk posed to these features. Furthermore, the incorporation of 3D GIS analysis, integrating 3D point cloud representations of the ground‐penetrating radar volume and DEM, provided essential insight into the state of preservation of the buried features. The collaborative application and joint interpretation of these methodologies yielded a wide range of clues and explanations, unravelling the complex palimpsest of past activities. This research not only serves as foundation for future studies specifically for Savatra, but also provides a preliminary remote sensing–based exploration blueprint to other yet to be studied archaeological sites.

Elaborating and performing optimization approaches for web-based 3D spatial analysis of 3D city models

ABSTRACT Three-dimensional (3D) geographic information systems (GIS) are vital for urban planning and architectural design, enabling detailed visualization and analysis of urban landscapes. However, the growing size of 3D city data and the transition to web environments cause performance challenges for real-time applications. This study addresses the need for optimizing 3D spatial analyses for the web using Quadtree data structures and multiprocessing tools. Optimization scenarios based on data size and hardware configurations are evaluated, and the best hosting plans for GIS applications on cloud services are also considered.

Streamlining BIM models for GIS integration: an advanced approach to building envelope extraction

ABSTRACT Building Information Modelling (BIM) models are crucial for urban building modeling in three-dimensional (3D) Geographic Information Systems (GIS); however, their intricate nature requires lightweight design for seamless GIS integration. Envelope extraction is a primary method for achieving lightweight BIM; nevertheless, it encounters challenges related to robustness and simplification. To tackle this issue, this study proposes a more effective envelope extraction method. Initially, BIM elements are categorized into three types based on their semantics: space-occupying, portal, and other facility elements. Subsequently, for space-occupying elements, a 3D alpha wrapping algorithm is employed to obtain a tightly enveloped triangular mesh and then simplified into a lightweight polygonal mesh. Finally, portal elements are replaced with simplified proxies and embedded into a polygonal mesh through extrusion operations to refine the model. The refined model is combined with other facility elements to achieve the final simplification result. Experiments were conducted using publicly available datasets and manually created BIM models of various types and complexities. The experimental results demonstrate that our method significantly reduces the data volume of BIM models while maintaining their accuracy. Furthermore, this approach does not necessitate strict geometric requirements for BIM building elements and allows for gaps between them, thereby exhibiting higher robustness.

Computer Simulation of Rural Landscape Design Based on Remote Sensing Image Technology

Introduction: The field of rural landscape design deals with the design and recovery of rural areas and landscape in a way that it can support natural biodiversity in addition to human needs in sustainable ways as well as maintaining its cultural character. It employs the use of plants, landforms and some water without interference with the environment in order to come up with useful and beautiful spaces. Recognizing the importance of preserving the environment while at the same time investing in developmental projects, it is centralized and focuses on the entire eco-system with the aim of enhancing the lives of the people. Aim: This study aims to develop theoretical aspect of an innovative computer simulation model for designing rural landscapes by applying the technology of remote sensing image. Research methodology: We suggest a new Starling Murmuration search-driven Adaptive YOLOv7 algorithm to identify and categorize several rural buildings and setting types. For the image data, we collected abundant data from several environments using UAV devices to train our proposed model. It is not surprising that our proposed model combined the use of three dimensional (3D) geographic information system (GIS) virtual imaging design model in the simulation of the rural landscape designs. Our recommended model is then extended using SM optimization to improve object detection with YOLOv7. By repeated adjustments of the network parameters in a somewhat similar fashion like flocking, we managed to enhance both accuracy and efficiency. This framework exploits crowdsourcing for delimiting rural buildings and landscapes with high-fidelity. Findings and Conclusion: We implemented our recommended model in Python software. During the phase of evaluation, we evaluate the efficacy of our recommended SM-AYOLOv7 model across a variety of parameters such as precision (91.72%), recall (92.34%), Intersection over Union (IoU) (90.23%), and f1 score (93.64%). Our experimental results precisely indicate that our approach outperforms traditional approaches. We demonstrate significant increases in accuracy and adaptability, especially when adjusting to dynamic configurations.

3D Point Cloud and GIS Approach to Assess Street Physical Attributes

The present research explores an innovative approach to objectively assessing urban streets attributes using 3D point clouds and Geographic Information Systems (GIS). Urban streets are vital components of cities, playing a significant role in the lives of their residents. Usually, the evaluation of some of their physical attributes has been subjective, but this study leverages 3D point clouds and digital terrain models (DTM) to provide a more objective perspective. This article undertakes a micro-urban analysis of basic physical characteristics (slope, width, and human scale) of a representative street in the historic centre of Valencia (Spain), utilizing 3D laser-scanned point clouds and GIS tools. Applying the proposed methodology, thematic maps were generated, facilitating the objective identification of areas with physical attributes more conducive to suitable pedestrian dynamics. This approach provides a comprehensive understanding of urban street attributes, emphasizing the importance of addressing their assessment through advanced digital technologies. Moreover, this versatile methodology has diverse applications, contributing to social sustainability by enhancing the quality of urban streets and open spaces.

Quick Extraction of Joint Surface Attitudes and Slope Preliminary Stability Analysis: A New Method Using Unmanned Aerial Vehicle 3D Photogrammetry and GIS Development

The present study proposes a preliminary analysis method for rock mass joint acquisition, analysis, and slope stability assessment based on unmanned aerial vehicle (UAV) photogrammetry to extract the joint surface attitude in Geographic Information Systems (GIS). The method effectively solves the difficulties associated with the above issues. By combining terrain-following photogrammetry (TFP) and perpendicular and slope surface photogrammetry (PSSP), the three-dimensional (3D) information can be efficiently obtained along the slope characteristics’ surface, which avoids the information loss involved in traditional single-lens aerial photography and the information redundancy of the five-eye aerial photography. Then, a semi-automatic geoprocessing tool was developed within the ArcGIS Pro 3.0 environment, using Python for the extraction of joint surfaces. Multi-point fitting was used to calculate the joint surface attitude. The corresponding attitude symbols are generated at the same time. Finally, the joint surface attitude information is used to perform stereographic projection and kinematic analysis. The former can determine the dominant joint group, and the latter can obtain the probability of four types of failure, including planar sliding, wedge sliding, flexural toppling, and direct toppling. The integrated stability evaluation method studied in this paper, which combines a 3D interpretation of UAV and GIS stereographic projection statistical analysis, has the advantages of being efficient and user-friendly, and requires minimal prior knowledge. The results can aid in the geological surveys of slopes and guide engineering practices.

A Single Data Extraction Algorithm for Oblique Photographic Data Based on the U-Net

In the automated modeling generated by oblique photography, various terrains cannot be physically distinguished individually within the triangulated irregular network (TIN). To utilize the data representing individual features, such as a single building, a process of building monomer construction is required to identify and extract these distinct parts. This approach aids subsequent analyses by focusing on specific entities, mitigating interference from complex scenes. A deep convolutional neural network is constructed, combining U-Net and ResNeXt architectures. The network takes as input both digital orthophoto map (DOM) and oblique photography data, effectively extracting the polygonal footprints of buildings. Extraction accuracy among different algorithms is compared, with results indicating that the ResNeXt-based network achieves the highest intersection over union (IOU) for building segmentation, reaching 0.8255. The proposed “dynamic virtual monomer” technique binds the extracted vector footprints dynamically to the original oblique photography surface through rendering. This enables the selective representation and querying of individual buildings. Empirical evidence demonstrates the effectiveness of this technique in interactive queries and spatial analysis. The high level of automation and excellent accuracy of this method can further advance the application of oblique photography data in 3D urban modeling and geographic information system (GIS) analysis.

Web-based platform to collect, share and manage technical data of historical systemic architectures: the Telegraphic Towers along the Madrid-Valencia path

Considering the variety of architectural Cultural Heritage typologies, systemic architectures require specific attention in the recovery process. The dimensions of "extension" and "recurrence" at geographic and technological levels affect the complexity of their knowledge process; they require systematic ways for their categorisation and comprehension to guarantee correct diagnosis and suitable rehabilitation. Recent applications involving Internet of Things (IoT) for the built Cultural Heritage have demonstrated the potentialities of three-dimensional (3D) geographic information system (GIS) models and structured databases in supporting complex degrees of knowledge for technicians, as well as management for administrators. Starting from such experiences, the work presents the setting up of a web-based platform to support the knowledge and management of systemic architectures, considering the geographical distribution of fabrics, natural and anthropic boundary conditions, and technical and administrative details. The platform takes advantage of digital models, machine and deep learning procedures and relational databases, in a GIS-based environment, for the recognition and categorisation of prevalent physical and qualitative features of systemic architectures, the recognition and qualification of dominant and recurrent decays and the management of recovery activities in a semi-automatic way. Specifically, the main digital objects used for testing the applied techniques and setting up the platform are based on Red-Green-Blue (RGB) and mapped point clouds of the historical Telegraphic Towers located along the Madrid-Valencia path, resulting from the on-site investigations. Their choice is motivated by the high level of knowledge about the cases reached in the last years by the authors, allowing them to test rules within the decision support systems and innovative techniques for their decay mapping. As the experience has demonstrated, the systematisation of technical details and operative pipeline of methods and tools allow the normalisation and standardisation of the intervention selection process; this offers policymakers an innovative tool based on traditional procedures for conservation plans, coherent with a priority-based practice.

An overview of predictive maintenance in relation to 2D and 3D Geographical Information System (GIS) for built environment

Well-preserved buildings can be a great asset to the country, contributing to its economic growth. The lifespan of a building and its assets can be extended through proper maintenance. This will improve the longevity of its function, sustain its performance, and optimize maintenance costs. Predictive maintenance is a proactive approach to maintenance, as it is conducted based on the current operational state of equipment rather than average life statistics. It is generally implemented in building maintenance, machinery, and many other industries. While predictive maintenance has grown in application with artificial intelligence, digital twins, and machine learning, its application with geographical information systems (GIS) and 3D GIS have limitedly discussed. Geospatial predictive maintenance can be realized by integrating the asset's location with its maintenance semantic and temporal information. By incorporating geospatial thinking into the predictive framework, it can help optimize decision-making processes such as allocating maintenance costs based on calculating affected areas, visualizing the location of assets, understanding their interactions with meteorological events and virtual situations. Therefore, this paper will discuss a review of predictive maintenance in relation to GIS and 3D GIS.

Scan to BIM Mapping Process Description for Building Representation in 3D GIS

This paper introduces a novel approach for mapping process description with Scan data to Building Information Modeling (BIM) in a 3D Geographic Information System (GIS). The methodology focuses on automatically generating building mass and facade information on the GIS platform using Point Cloud Data (PCD) of Airborne Laser Scanning (ALS). Advanced scanning techniques capture detailed geometry from the physical site and generate high-resolution point clouds, which are processed to create 3D models for GIS integration. The critical contribution of this research lies in a scalable Scan to BIM mapping process, which can be used for generating building footprints and masses, including attributes, on 3D GIS. The resulting integrated BIM–GIS dataset provides an accurate building mass, facade information, facility asset management, and architectural design and facilitates improved decision-making in urban planning based on enhanced visualization, analysis, and simulation. This study suggests a flexible Scan to BIM mapping process description based on use cases, including algorisms. Through prototype development, a case study demonstrates the effectiveness of the process approach, the automatic generation of BIM on a 3D GIS platform, and reducing the manual efforts. The proposed method automatically creates DEM, SHP, GeoJSON, IFC, and coordinate system information from scan data and can effectively map building objects in 3D GIS.

Machine-Learning-Enhanced Procedural Modeling for 4D Historical Cities Reconstruction

The generation of 3D models depicting cities in the past holds great potential for documentation and educational purposes. However, it is often hindered by incomplete historical data and the specialized expertise required. To address these challenges, we propose a framework for historical city reconstruction. By integrating procedural modeling techniques and machine learning models within a Geographic Information System (GIS) framework, our pipeline allows for effective management of spatial data and the generation of detailed 3D models. We developed an open-source Python module that fills gaps in 2D GIS datasets and directly generates 3D models up to LOD 2.1 from GIS files. The use of the CityJSON format ensures interoperability and accommodates the specific needs of historical models. A practical case study using footprints of the Old City of Jerusalem between 1840 and 1940 demonstrates the creation, completion, and 3D representation of the dataset, highlighting the versatility and effectiveness of our approach. This research contributes to the accessibility and accuracy of historical city models, providing tools for the generation of informative 3D models. By incorporating machine learning models and maintaining the dynamic nature of the models, we ensure the possibility of supporting ongoing updates and refinement based on newly acquired data. Our procedural modeling methodology offers a streamlined and open-source solution for historical city reconstruction, eliminating the need for additional software and increasing the usability and practicality of the process.

Automatic Generation of 3D Indoor Navigation Networks from Building Information Modeling Data Using Image Thinning

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.

Using digital technologies to plan and manage the pipelines network in city

AbstractPromoting the intelligent level of urban pipelines network planning and management is an important way to improve the rational development and utilisation of urban underground space and ensure the safe operation of urban infrastructure systems. The research on using the rapid and visual expression technology of 3D pipelines network model data, which has the functions of techno‐economic indexes calculation and automatic comparison of underground pipeline construction projects, effectively avoids possible errors in manual calculation and comparison and improves the efficiency and accuracy of the digital declaration process for project planning. In the 3D Geographic Information Systems (GIS) platform environment, this research help to develop many analysis and approval functions based on the data of the underground pipelines network model and the integrated pipes gallery Building Information Modelling, such as evaluating the compliance, feasibility and scientific of the planning scheme of the underground pipeline construction project, comparing the completion data with the data approved by the administrative departments of the project, evaluating the consistency of them and judging the compliance of the project completion data. The application results of some actual projects cases show that this research can improve the efficiency of planning declaration and approval analysis.

Dynamic Fusion Technology of Mobile Video and 3D GIS: The Example of Smartphone Video

Mobile videos contain a large amount of data, where the information interesting to the user can either be discrete or distributed. This paper introduces a method for fusing 3D geographic information systems (GIS) and video image textures. For the dynamic fusion of video in 3DGIS where the position and pose angle of the filming device change moment by moment, it integrates GIS 3D visualization, pose resolution and motion interpolation, and proposes a projection texture mapping method for constructing a dynamic depth camera to achieve dynamic fusion. In this paper, the accuracy and time efficiency of different systems of gradient descent and complementary filtering algorithms are analyzed mainly by quantitative analysis method, and the effect of dynamic fusion is analyzed by the playback delay and rendering frame rate of video on 3DGIS as indicators. The experimental results show that the gradient descent method under the Aerial Attitude Reference System (AHRS) is more suitable for the solution of smartphone attitude, and can control the root mean square error of attitude solution within 2°; the delay of video playback on 3DGIS is within 29 ms, and the rendering frame rate is 34.9 fps, which meets the requirements of the minimum resolution of human eyes.

The Conquense transhumance route in Spain described by 3D geographical information systems, GPS and remote sensing data

In semi-arid regions, such as the Mediterranean, seasonal changes in climate make the migration of ruminants essential for the efficient use of ecosystemic primary productivity. Transhumance is one of the oldest sheep husbandry systems which contributes to the conservation of natural biodiversity and to minimize the effects of climate change on livestock production. However, its use has decreased substantially, mainly because of livestock intensification. In Spain, particularly, interest in maintaining the traditional transhumant network has increased. Geotechnologies such as Global Positioning Systems (GPS) and Geographical Information Systems (GIS) might provide important information about the drove roads of the transhumant network. Therefore, in this study, we used several geotechnologies that described accurately and in detail the path followed by several sheep flocks and cattle herds in eastern Spain. Specifically, we monitored a Merino de los Montes Universales (MMU) sheep flock on the well-known Spanish Conquense route in Nov 2021. The flock traveled 349.8 km and climbed a total elevation of 1223 m in 23 days, moving 9–10 h per day. Maximum and minimum daily distances traveled were 19.06 km and 8.85 km, respectively, and the steepest slopes were encountered in the first few days of the journey, up to 60 %. The preferred land use/land covers (LU/LC) were scrublands, rainfed herbaceous crops, and rangelands. Indirectly, the study demonstrated the outstanding physiological aptitude of the MMU breed for withstanding the long journey, even when pregnant.

Three‐Dimensional Data Preparation and Immersive Mission‐Spanning Visualization and Analysis of Mars 2020 Mastcam‐Z Stereo Image Sequences

AbstractThe Mars 2020 Mastcam‐Z stereo camera investigation enables the generation of three dimension (3D) data products needed to visualize and analyze rocks, outcrops, and other geological and aeolian features. The Planetary Robotics Vision Processing framework “PRoViP” as well as the Instrument Data System on a tactical—sol‐by‐sol—timeframe generate 3D vision products, such as panoramas, distance maps, and textured meshes. Structure‐from‐motion used by the Advanced Science Targeting Toolkit for Robotic Operations (ASTTRO) “Landform” tool and long baseline stereo pipelines add to the 3D vision products' suite on various scales. Data fusion with textured meshes from satellite imagery and 3D data analysis and interpretation of the resulting large 3D data sets is realized by visualization assets like the Planetary Robotics Vision 3D Viewer PRo3D, the 3D Geographical Information System GIS CAMP (Campaign Analysis Mapping and Planning tool), the ASTTRO 3D data presentation and targeting tool, and the Mastcam‐Z planning tool Viewpoint. The pipelines' workflows and the user‐oriented features of the visualization assets, shared across the Mars 2020 mission, are reported. The individual role and interplay, complements and synergies of the individual frameworks are explained. Emphasis is laid on publicly available 3D vision data products and tools. A representative set of scientific use cases from planetary geology, aeolian activity, soil analysis and impact science illustrates the scientific workflow, and public data deployment modes are briefly outlined, demonstrating that 3D vision processing and visualization is an essential mission‐wide asset to solve important planetary science questions such as prevailing wind direction, soil composition, or geologic origin.

Integration of Remote-Sensing Techniques for the Preventive Conservation of Paleolithic Cave Art in the Karst of the Altamira Cave

Rock art offers traces of our most remote past and was made with mineral and organic substances in shelters, walls, or the ceilings of caves. As it is notably fragile, it is fortunate that some instances remain intact—but a variety of natural and anthropogenic factors can lead to its disappearance. Therefore, as a valuable cultural heritage, rock art requires special conservation and protection measures. Geomatic remote-sensing technologies such as 3D terrestrial laser scanning (3DTLS), drone flight, and ground-penetrating radar (GPR) allow us to generate exhaustive documentation of caves and their environment in 2D, 2.5D, and 3D. However, only its combined use with 3D geographic information systems (GIS) lets us generate new cave maps with details such as overlying layer thickness, sinkholes, fractures, joints, and detachments that also more precisely reveal interior–exterior interconnections and gaseous exchange; i.e., the state of senescence of the karst that houses the cave. Information of this kind is of great value for the research, management, conservation, monitoring, and dissemination of cave art.

Visual supervision of large‐scope heat source factories based on knowledge graph

AbstractAs a result of limited resources and economic development acting as dual bottleneck constraints, optimizing industrial layout structures has been a general trend in heavy industry. The visual supervision of heat source factories based on integrated multidimensional information is important for optimizing an industrial layout. Based on Visible Infrared Imaging Radiometer Suite (VIIRS) I‐band 375‐m active fire product (VNP14IMG) data, point of interest data, enterprise attribute information and other data, combined with clustering regression, knowledge graphs (KGs), 3D geographic information systems, and other technical methods, the temporal and spatial variations in China's heat source industries are macroscopically analyzed, and a visual supervision platform for heat source industries with functions such as visualization, time‐series analysis, and knowledge discovery is established. The results show that: (1) overall, heat source factories exhibit a spatial pattern of dense in the east and sparse in the west, and the number of industrial heat source objects and the number of industrial fire hotspots decreased from 2013 to 2021, with rates of decline of 22.0 and 27.3%, respectively; (2) the enterprise KG, which contains basic enterprise information, dynamic enterprise risk information and enterprise equity structure information, can provide users with accurate and reliable enterprise knowledge; and (3) the remote sensing monitoring information platform for heat source factories performs well in terms of the discovery and management of heat source factories at large scale. In general, the platform constructed in this study can support the rapid monitoring and positioning of industrial heat sources over large areas to improve supervision in terms of finding problems and preventing risks and to provide a necessary reference for optimizing industrial spatial patterns and environmental governance.

Towards Digital Twinning on the Web: Heterogeneous 3D Data Fusion Based on Open-Source Structure

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.