Andri Hernandi

Level of Detail 1 (LOD1) building models underpin national 3D cadastre development, yet OpenStreetMap (OSM) and Global Building Atlas (GBA) have rarely been validated against authoritative cadastral data using designs that separate positional, geometric, and completeness errors. Both were evaluated against Integrated Complete Systematic Land Registration (PTSL Terintegrasi) footprints in Banjarsari, a low-rise semi-urban Indonesian settlement, using a stratified cluster sample of 189 reference buildings. A polygon-level provenance audit showed that 73.1% of sampled GBA polygons were geometrically identical to OSM, leaving only Google-sourced polygons independent. OSM carried a systematic 4.0 m offset; correcting it raised mean Intersection over Union from 0.29 to 0.61 and reversed the shape ranking, with OSM overtaking Google (0.42). Fusion lowered design-weighted omission from 31.3% to 21.1%. GBA heights were nearly unbiased (+0.23 m) yet less accurate than guessing the sample mean, beating that baseline only after local recalibration. Findings held under radius, threshold, and quality-assurance sensitivity tests. Neither source is directly adequate as LOD1 cadastral geometry without source-specific remediation. Methodologically, the study contributes a two-axis framework partitioning error by remediability, provenance auditing that prevents circular comparison, and design-based estimation. Practically, classifying defects by remediation cost leaves 21% field capture as the alternative to digitizing 100%.

Indigenous knowledge and associated indigenous resource management practices are at the root of sustainable land and marine management. Typically, they point to the necessity of maintaining biodiversity and of ensuring the sustenance of social and economic systems, which benefit the well-being of indigenous communities. Conscious of these core attributes, the Government of Indonesia has enabled formal access for indigenous communities to forests for their livelihoods. Nonetheless, meeting the sustainable development goals through such forest management and conservation in Indonesia is threatened by various competing interests and power imbalances. These lead to the disproportionate conversion of naturally vegetated areas, as well as the inability of communities to benefit from economic opportunities. Moreover, the Government of Indonesia has insufficiently regulated the utilisation of indigenous knowledge to conserve the forest areas. This creates a policy design and implementation gap which is not properly understood or addressed. In this conceptual article, we posit that applying cultural space methodology fills the gaps. This methodology combines cultural space and land administration concepts and connects people to land and marine space. This article discusses how and why using the methodology proves to be effective for agricultural and maritime communities in Indonesia and helps to reform the administration capacities of the territories. It identifies and assesses people and land/marine space relationships by the existence of (1) knowledge, practices, and/or objects that represent the relationship, (2) the social, economic, and environmental function of space for the community, and (3) administration of the forest and conservation areas. The methodology also provides a procedure to convert information on the interrelation of the indigenous community, its cultural space in the forest and conservation areas, and indigenous knowledge into geospatial information and data that represent the cultural space unit as a geographic feature.

Property taxation serves as a critical instrument for fiscal efficiency and equitable distribution, yet implementation faces significant challenges including valuation inaccuracies, insufficient administrative capacity, and diminished public trust. Indonesia’s Land and Building Tax (PBB-P2) utilizes the Sales Value of Taxable Objects (NJOP) as an administrative proxy for market value, which frequently deviates from actual land prices. These disparities create horizontal inequities, diminish local revenue potential, and generate taxpayer resistance, especially in decentralized regions with constrained technical resources. This research presents the Spatially Adaptive Land Value Determination (SALAD) model as a comprehensive framework for enhancing property tax governance and modernizing fiscal cadastre systems. Unlike conventional mass appraisal methods, SALAD integrates spatial zoning, assessment ratio analysis, land-use characteristics, and the Index of Developing Villages (IDM) with socio-economic indicators including purchasing power and community fiscal behavior. The model incorporates structured social validation to improve public acceptance. Field validation in Lebak Regency employed mixed-methods design with surveys of 75 respondents across 20 villages and interviews with village heads and tax officials. Results demonstrate that transparency, fairness, and visible public benefits are essential for community support. Validation indices vary significantly by IDM category (ANOVA: F = 4.23, p = 0.03 for economic; F = 3.81, p = 0.04 for institutional), confirming that the SALAD model’s adaptive mechanism is empirically grounded.

Before the development of 3D cadastre, cadastral systems were based on 2D representations, which now require transformation or updating. In this context, the first issue is that existing 2D rights are not aligned with recent 3D data acquired using advanced technologies such as Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR). The second issue is that point clouds of objects captured by UAV-LiDAR, such as fences and exterior building walls—are often neglected. However, these point cloud objects can be utilized to adjust 2D rights to correspond with recent 3D data and to update 3D building models with a higher level of detail. This research leverages such point cloud objects to automatically generate 3D rights and building models. By combining several algorithms, such as Iterative Closest Point (ICP), Random Forest (RF), Gaussian Mixture Model (GMM), Region Growing, the Polyfit method, and the orthogonality concept—an automatic workflow for generating 3D cadastral models is developed. The proposed workflow improves the horizontal accuracy of the updated 2D parcels from 1.19 m to 0.612 m. The floor area of the 3D models improves by approximately ±3 m². Furthermore, the resulting 3D building models provide approximately 43% to 57% of the elements required for 3D property valuation. The case study of this research is in Indonesia.

Forensic cadastre is the application of forensic science in land dispute resolution to enhance the accuracy of analysis and the precision of recommendations. This study applied forensic science stages—recognition, identification, individualization, and evidence evaluation—to a land overlap dispute between state-owned land and privately owned parcels belonging to three individuals in Buntu Village, Ligung District, Majalengka Regency, West Java. Method validation was conducted through expert judgment using a questionnaire to assess its alignment with scientific and professional dispute resolution principles. The reliability test resulted in a Cronbach’s Alpha value of 0.855 for 10 questionnaire items, indicating a high level of internal consistency, while the validity reached 80%. These findings confirm that forensic cadastre has a strong conceptual validity and good reliability, making it an objective and transparent strategy for resolving land disputes that cannot be settled through traditional methods such as mediation.

This study examines the conversion strategy of 2D to 3D cadastral maps by standardizing the height limits of land rights based on LU/LC. To achieve 3D cadastral maps, the research proposes a conversion strategy considering height factors. The height dimension of cadastral maps faces challenges in determining maximum heights for features like buildings, given varying regional regulations. As a solution, the concept of surface feature height (SFH) is applied along with LU/LC classification. Economic considerations, such as state revenue from taxes, are also factored into the proposed height limits. The results indicate that building/property heights in Bekasi Regency show significant development potential. In the residential sector, the maximum height reaches 24 m, lower than Bekasi City (48 m) and Bandung City (30 m). In the industrial sector, while heights can reach 25 m, the regulatory limit is only 9 m, posing challenges for investment. In the commercial sector, the maximum height can reach 45 m, but the low regulatory limit of 10 m restricts further development. This research provides a foundation for policy development and an effective 3D cadastral system, emphasizing the need for Bekasi Regency to re-evaluate its building height regulations to maximize its development potential.

Responding to cost inefficiency in the Indonesian cadastral mapping system, this study aimed to analyze the implementation of integrated mapping activities, namely complete systematic land registration, assessing land value zones, and regional land stewardship balance. This study employed an operational management system, particularly focusing on financial aspects, using data envelopment analysis (DEA), a non-parametric technique for evaluating the relative efficiency of decision-making units. These approaches are rarely explored in cadastral mapping. DEA was used to analyze the efficiency of seven aspects: aerial mapping, office supplies, meetings, consumption, transportation, capital expenses, and socialization. Content analysis was used to identify integration parameters derived from operational management-based integration. Cronbach’s alpha was used for the reliability test. The Way Sulan sub-district of South Lampung Regency in Lampung Province, Indonesia, was selected as the study area due to its complete mapping activities. The findings suggested that applying operational management for integrated cadastral mapping is effective. However, contrary to expectations, efficiency was lower in dense urban areas, where costs tend to be cheaper, while efficiency was higher in agricultural areas, where expenses were much greater. Based on this study, an operational management approach to integrated cadastral mapping is recommended to improve budget efficiency and general standards of land management, especially in areas with complex land use.

Land taxation, mainly the Land and Building Tax (LBT), is a critical revenue source for local governments in Indonesia, yet its contribution remains suboptimal due to inefficiencies in assessment and administration. This study focuses on the Lebak Regency to optimize the Sales Value of Taxable Objects (SVTO) assessment, a key determinant of LBT revenue. Using spatial analysis, Land Value Zones (LVZ), and socio-economic data, the research evaluates assessment ratios to identify discrepancies between SVTO and market values. Adjustments to SVTO are proposed based on spatial patterns, such as land use and infrastructure, and the Index of Developing Villages (IDV). Findings reveal significant disparities between assessed and market values, with assessment ratios ranging from 1% to 267%. Simulations indicate potential LBT revenue of IDR 224.7 billion under full compliance, compared to IDR 44.22 billion, the 2024 target. Adjustments based on spatial patterns from land use planning and village development indices enhance equity and accuracy in tax assessments, optimizing local revenues. Despite these improvements, the study’s limitations lie in the lack of community validation for the proposed methodology, which is essential to confirm its practicality and acceptance. Future research should address this gap and explore household-level dynamics, including tax affordability and spending patterns, to enhance policy inclusivity and align taxation systems with local socio-economic realities.

Land registration is an important program in asset legalization with the vast land resources owned by Indonesia. The reality is that there are 48 million certificated lands out of an estimated 126 million certificates throughout Indonesia, so the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) made a breakthrough through the Complete Systematic Land Registration (PTSL) program. The grouping mechanism of the PTSL program is divided into four clusters, namely K-1, K-2, K-3, and K-4. Land parcels included in K-4 have problems, namely that they have been registered but not mapped/mapped incorrectly, so the idea of modeling the K-4 typology is needed for accelerating the improvement in land data quality (KW). The research location is in Bandung Regency, which is included in the top five land offices with the highest number of K-4 in West Java Province. This research method uses a mixed method, namely quantitative with a Slovin approach for the identification and clustering of K-4 typology and descriptive qualitative for justification of typology modeling in multiple aspects. The results of K-4 typology modeling were 128 clusters based on seven types of data criteria and obtained 4 clusters that matched the data sample, namely T-1, T-43, T-63, and T-128. The four clusters were justified against the K-4 concept in the Ministry of ATR/BPN, the FFP-LA concept, and the PESTLE framework. Dissemination of K-4 typology modeling is a breakthrough that can be implemented by the Ministry of ATR/BPN and its staff in various regions and the role of multi-concepts in this research can be an input for improving the K-4 concept that has been in effect so far.

Airborne laser technology produces point clouds that can be used to build 3D models of buildings. However, the work is a laborious process that could benefit from automation. Artificial intelligence (AI) has been widely used in automating building segmentation as one of the initial stages in the 3D modeling process. The algorithms with a high success rate using point clouds for automatic semantic segmentation are random forest (RF) and PointNet++, with each algorithm having its own advantages and disadvantages. However, the training and testing data to develop and test the model usually share similar characteristics. Moreover, producing a good automation model requires a lot of training data, which may become an issue for users with a small amount of training data (limited data). The aim of this research is to test the performance of the RF and PointNet++ models in different regions with limited training and testing data. We found that the RF model developed from a small amount data, in different regions between the training and testing data, performs well compared to PointNet++, yielding an OA score of 73.01% for the RF model. Furthermore, several scenarios have been used in this research to explore the capabilities of RF in several cases.