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.

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.

Parcels data in Indonesia are still stored in 2-dimensional (2D) geometry which are integrated with other attribute data, such as the case with the Directorate of Land and Building Tax Indonesia. Whereas, building taxes assessment refers to a number of details that require the information to be stored in 3-dimensional (3D) forms. This study aims at the use of Building Information Modeling (BIM) technology, which widely used in building asset management in 3D. This research illustrates the usability of the role of BIM in assessing and managing building taxes in Indonesia. The point clouds were obtained using Terrestrial Laser Scanner (TLS) technology. The point clouds processed so that it can form 3-dimensional geometrical apartment. The attributes of the 3-dimensional model integrated with the geometric model using the BIM concept. The results analyzed to assess whether BIM concept was able to fulfill the needs of the 3D fiscal cadastre in Indonesia.

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.

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.