Developers have long used game engines for visualizing virtual worlds for players to explore. However, using real-world data in a game engine is always a challenging task, since most game engines have very little support for geospatial data. This paper presents our findings from exploring the Unity3D game engine for visualizing large-scale topographic data from mixed sources of terrestrial laser scanner models and topographic map data. Level of detail (LOD) 3 3D models of two buildings of the Universitas Gadjah Mada campus were obtained using a terrestrial laser scanner converted into the FBX format. Mapbox for Unity was used to provide georeferencing support for the 3D model. Unity3D also used road and place name layers via Mapbox for Unity based on OpenStreetMap (OSM) data. LOD1 buildings were modeled from topographic map data using Mapbox, and 3D models from the terrestrial laser scanner replaced two of these buildings. Building information and attributes, as well as visual appearances, were added to 3D features. The Unity3D game engine provides a rich set of libraries and assets for user interactions, and custom C# scripts were used to provide a bird’s-eye-view mode of 3D zoom, pan, and orbital display. In addition to basic 3D navigation tools, a first-person view of the scene was utilized to enable users to gain a walk-through experience while virtually inspecting the objects on the ground. For a fly-through experience, a drone view was offered to help users inspect objects from the air. The result was a multiplatform 3D visualization capable of displaying 3D models in LOD3, as well as providing user interfaces for exploring the scene using “on the ground” and “from the air” types of first person view interactions. Using the Unity3D game engine to visualize mixed sources of topographic data creates many opportunities to optimize large-scale topographic data use.

During the last decade, we saw an explosion of geospatial data being produced. Most of which coming from GPS-enabled devices available for general consumers. The large amount of geotagged data coined the term `Geospatial Big Data', indicating the semi-structured and unstructured nature of such data. SQL relational databases have been known in the past to handle geospatial data very well. However, the abundance of geospatial big data pushed forward the need for NoSQL database which is expected to perform better in terms of handling and storing geospatial big data. This paper discusses the quantitative comparison of performance between the SQL (i.e., PostGIS) and NoSQL (i.e., MongoDB) databases in handling geospatial big data. A NodeJS-based angular-framework web app was developed to test the real-world performance of MongoDB and PostGIS in handling a large amount of simulated geospatial data. A different number of points were generated for testing the geospatial data storing and loading capability of both the databases. The test was conducted by comparing the result of XHR (XML HTTP Request) of both databases in each case. The result showed that NoSQL database, i.e. MongoDB, performs better in loading big geospatial data compared to traditional SQL database using PostGIS.

Major cities and urban areas are beginning to develop and use 3D properties and public facilities. Consequently, 3D cadastral surveys are increasingly being employed for strata unit ownership registration as a part of land administration services. At present, most national land information systems do not support 2D and 3D cadastral visualizations. A field survey or validation survey is required to determine the geometry of 3D spatial units for property registration. However, the results of 3D surveys and mapping are not stored in the land information system. This work aims to integrate 2D and 3D geospatial data of property units collected from cadastral surveys with their corresponding legal data. It reviews the workflow for the use of 3D survey data for first-titling of 3D properties in Indonesia. A scenario of use and a prototype were developed based on existing practices and the possibility of extending Indonesia’s Land Administration Domain Model (LADM) to represent 3D units. Data submitted to the prototype as 3D geometries was survey data from 3D cadastral surveys or validation surveys utilizing terrestrial survey methods. The prototype used PostGIS and Cesium Ion to store 3D geometries of data from six 3D surveys. Registrars in local land offices could use the prototype to undertake strata unit registration that establishes a relationship among geospatial features and their survey documents and legal documents. Cesium JS was used as a 3D browser, customized as a web application, to manage and visualize 3D survey data to support strata title registration. The results demonstrate that the first titling of 3D cadaster objects could be conducted and properly visualized in Indonesia by extending the existing LADM with more support for 3D spatial representations and survey documents.

This paper is focused to address the map display usability for finding given POI addresses in a popular urban city area. LOD 1 of 3D representations of city buildings are presented into a 2.5D map for pedestrian navigation test. This 3D map display is evaluated against familiar 2D map system on the test participants’ smartphones. 16 participants were involved in the field test. The typical walking model of a searching task that is focused only to look for a certain address of building is chosen as the way finding model during the field test. Three kinds of navigation processes i.e. self-orientation, spatial knowledge acquisition and navigation decision for searching task were evaluated for each test participant. Usability measures of 3D map-based display over 2D-map based display for pedestrian navigation were collected from test participants’ mobile devices. In addition to that, activities of test participants in terms of acceleration and orientation information are used to support analysis of pattern and trends of test participants. As the testing app is also intended to support smart city application, its ability to provide user report on complaints was also assessed. Most participants agreed with the statements in the questionnaire that were organized into three sections, i.e. addressing participants’ interaction, participants’ responses in navigation processes and crowdsensing. The results suggest that 3D map-based pedestrian navigation is more usable to be used to look for a certain address of building in central tourist area of urban city.

Developing a 3D city model is always a challenging task, whether on how to obtain the 3D data or how to present the model to users. Lidar is often used to produce real-world measurement, resulting in point clouds which further processed into a 3D model. However, this method possesses some limitation, e.g. tedious, expensive works and high technicalities, which limits its usability in a smaller area. Currently, there exists pipeline utilize point-clouds from Lidar data to automate the generation of 3D city model. For example, 3dfier (http://github.com/tudelft3d/3dfier) is a software capable of generating LoD 1 3D city model from lidar point cloud data. The resulting CityGML file could further be used in a 3D GIS viewer to produce an interactive 3D city model. This research proposed the use of Structure from Motion (SfM) method to obtain point cloud from UAV data. Using SfM to generate point clouds means cheaper and shorter production time, as well as more suitable for smaller area compared to LiDAR. 3Dfier could be utilized to produce 3D model from the point cloud. Subsequently, a game engine, i.e. Unity 3D, is utilized as the visualization platform. Previous works shows that a game engine could be used as an interactive environment for exploring virtual world based on real-world measurement and other data, such as parcel boundaries. This works shows that the process of generating 3D city model could be achieved using the proposed pipeline.

Given a series of overlapped images, it is possible to reconstruct camera position and orientation, as well as 3D coordinates of object in the images. Such method is known as Structure from Motion (SfM), which is currently implemented in various commercial and open source software. The first software commonly don't provide Sfm algorithm. While the latter software are commonly developed partially. This research aims to perform a SfM reconstruction using a stack of open source SfM software, with each software serves different purposes in producing 3D models from images. A python script were employed to produce seamless integration of the software. Geometric evaluations were performed to the result of SfM reconstruction on three datasets obtained using mobile handheld camera. The deliverables of the stack were compared to result of commercial software using cloud-to-cloud comparison to obtain geometric closeness of both the results. Ground truth measurement were conducted to assess the positional consistency in SfM reconstruction on different dataset. The test showed that 3D models resulted from the open source stack perform on pair with the result of Agisoft Photoscan as indicated by the small RMSE value obtained from cloud-to-cloud comparison (0.019818 m and 0.350701 m for two datasets, respectively). The result of ground-truth evaluation indicates large error possibly due to the homogeneity in the dataset used, as indicated by the camera pose graph.