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.

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.

The utilization of the altimetric satellite’s on-board radiometer for atmospheric observation is demonstrated. This study uses the Wet Tropospheric Correction (WTC) data from the Topex/Jason altimetry mission series (Topex/Poseidon, Jason-1, Jason-2/OSTM, and Jason-3). The data spans nearly 30 years, making them sufficient for climate study. Precipitable Water Vapor (PWV) is derived from the WTC and used to study the water vapor variability over the Tropical Indian Ocean (TIO). Standard EOF (Empirical Orthogonal Function) analysis on the derived PWV anomaly (PWVA) in the TIO generates two leading modes. The first mode has a dipole spatial structure that explains 18.3% of the total variance, and the second one has a basin-wide homogeneous structure that explains 12.3% of the total variance. The correlation coefficient values between the principal components with DMI (Dipole Mode Index) and ONI (Oceanic Niño Index), a monitoring index for IOD (Indian Ocean Dipole) and ENSO (El Niño Southern Oscillation), respectively, suggest that the dipole mode is associated with both IOD and ENSO and the second one is associated only with ENSO but with 6 months lag, indicating that it’s actually a delayed response to ENSO. This delayed response is further described in a teleconnection map that shows the maximum lag-correlation of ONI with PWVA and the associated lag time. Furthermore, composite maps of IOD and ENSO for both positive and negative phases is generated to describe and distinguish their spatio-temporal characteristics. It is found that the PWVA field during extreme IOD phase has a dipole spatial structure similar to the Sea Surface Temperature (SST) Anomaly (SSTA) field as shown in previous studies, suggesting that the SST-PWV feedback has a significant role in the IOD mechanism. On the contrary, during extreme ENSO phases, the PWVA field has a zonal gradient spatial structure which makes it similar to the dipole structure of IOD but with one clear difference being the absence of the extreme peak located in the middle of the dipole region. This zonal gradient is believed to be associated with the weakening (strengthening) of the convergence zone over Indonesia during El Niño (La Niña).A PWVA-derived index is generated. This index adopt the same computation method as the DMI but with PWVA instead of SST, which then can be used to monitor the activity of the Indian Ocean Walker Circulation (IOWC). This index, together with ONI and DMI, is analyzed to see how the IOWC got modulated by ENSO and IOD. It is found that an extreme DMI value is almost always accompanied by a high PWVDG, with which the amplitude is also influenced by the ONI. In other words, the IOWC is modulated strongly with the activity of IOD and the ENSO acts to weaken or strengthen the effect. This showcased the feasibility of using the data measured by the radiometer of altimetric satellites for atmospheric studies, a potential continuation of this research would be the use of the synchronous altimeter-radiometer measurement for more advanced analysis such as the ocean-atmosphere coupling.

The mean temperature weighted with water vapor pressure (Tm) is an important parameter to obtain precipitable water vapor (PWV) from the Global Navigation Satellite Systems (GNSS) observations. This study investigates the possible impacts of equatorial troposphere on Tm estimates and its relation with surface temperature Ts. We calculated Tm in Indonesia from a Numerical Weather Model at nine InaCORS sites. We used 3-hourly ERA5 pressure, temperature, and humidity profiles for the year 2019. We found that Tm and surface temperature Ts in Indonesia have low correlation, less than 0.4. Seasonal and site-specific Tm-Ts relationships have slightly higher correlation, although the values can vary significantly. The highest correlation of around 0.7 is found at site CPUT in Kalimantan. We calculated Tm at nine additional stations in Kalimantan and found that stations located farther from the coast tend to have higher correlation, independent of the seasons. This suggests that Tm is also influenced by the vicinity to the coast. Based on our findings, the use of a general Tm-Ts relationship in Indonesia may not be appropriate. Further studies are necessary to develop an improved Tm over Indonesian region.