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.

In 2020, digital transformation was a major theme to commemorate Indonesia’s main agrarian law’s anniversary. This theme is a reminder of the need to fully implement digital services to improve the quality of land registration products that are cheap, easy to operate, perform quickly, and are trusted by the community. However, no research has comprehensively assessed the readiness of the digital transformation of land services in Indonesia. This paper aims to evaluate the readiness of a land office to achieve digital transformation visions. Here, we apply the Digital Governance Assessment Framework (DGRA), adapted to the land service sector, as the basis for conducting this evaluation. The nine core indicators of the DGRA toolkit are used as a basis for assessment. Desk studies were conducted to identify formal legislation and to find the technical specifications. Direct observations and in-depth interviews were conducted with stakeholders to find user needs and evaluate the implementation of current regulations on the land service business process. Quality assessment was carried out on land registration data at the Land Office of Yogyakarta City as a sample. The quality assessment results indicate a problem with completeness, conformity, consistency, accuracy, duplication, and integrity. In conclusion, the readiness level still needs improvement, especially in the indicator related to Cyber Security, Privacy, and Resilience (1.0). Even though the Leadership and Governance, User-Centered Design, and Public Administration Reforms and Change Management sections shows a reasonably high score (≥2.0), other core sections, namely Technology Infrastructure (1.7), Legislation and Regulation (1.4), Data Infrastructure, Strategies, and Governance (1.8) are mediocre, and therefore they need improvement.

This paper presents the results from a quality and usability analysis of participatory land registration (PaLaR) in Indonesia’s rural areas, focusing on data quality, cost, and time. PaLaR was designed as a systematic community-centered land titling project collecting requisite spatial and legal data. PaLaR was piloted in two communities situated in Tanggamus and Grobogan districts in Indonesia. The research compared spatial data accuracy between two approaches, PaLaR and the normal systematic land registration approach (PTSL) with respect to point accuracy and polygon area. Supplementary observations and interviews were undertaken in order to evaluate the effectiveness of the spatial and legal data collection, as well as logical consistency of the data collected by the community committee, using a mobile application. Although the two pilots showed a lower spatial accuracy than the normal method (PTSL), PaLaR better suited local circumstances and still delivered complete spatial and legal data in a more effective means. The accuracy and efficiency of spatial data collection could be improved through the use of more accurate GNSS antennas and a seamless connection to the national land databases. The PaLaR method is dependent on, amongst other aspects, inclusive and flexible community awareness programs, as well as the committed participation of the community and local offices.

In 2017, the Indonesian government implemented the systematic land registration (PTSL) process, projected to be finished by 2025. However, this process faces some challenges in the spatial and legal data collection process, resulting in the Indonesian cadastral system still being incomplete. For instance, during the three years of its implementation, out of about 135 million parcels, only 49.5% have been registered. Therefore, the level of completeness needs to be improved. This research aims to assess the compliance of the fixed boundary process’ legal elements, such as the parties that locate the boundary, agreement between the adjoining landowners, and boundary markers. This is a piece of qualitative research in which the data were obtained through interviews from questionnaire surveys to land administration policymakers. Subsequently, the research carried out regulation assessments to develop a country-context cadastre typology of the current cadastral mapping activities. Data were obtained from the results of the PTSL campaign in the Madiun regency. The result showed that the high percentage (i.e., 96.61%) of legal elements regarding the boundary agreement in a rural area could be used as a potential enabler towards achieving completion of the Indonesian cadastre.

High-accuracy peat maps are essential for peatland restoration management, but costly, labor-intensive, and require an extensive amount of peat drilling data. This study offers a new method to create an accurate peat depth map while reducing field drilling data up to 75%. Ordinary least square (OLS) adjustments were used to estimate the elevation of the mineral soil surface based on the surrounding soil parameters. Orthophoto and Digital Terrain Models (DTMs) from LiDAR data of Tebing Tinggi Island, Riau, were used to determine morphology, topography, and spatial position parameters to define the DTM and its coefficients. Peat depth prediction models involving 100%, 50%, and 25% of the field points were developed using the OLS computations, and compared against the field survey data. Raster operations in a GIS were used in processing the DTM, to produce peat depth estimations. The results show that the soil map produced from OLS provided peat depth estimations with no significant difference from the field depth data at a mean absolute error of ±1 meter. The use of LiDAR data and the OLS method provides a cost-effective methodology for estimating peat depth and mapping for the purpose of supporting peat restoration.