Sustainable development is contingent upon the efficient management of land resources for resolving spatial challenges such as land-use conflicts and fragmentation. A land-suitability model offers a potential instrument for assessing land-use/land-cover (LULC) consistency with spatial plans. This study employed a data-driven probabilistic approach using a support vector machine (SVM) algorithm and error-correcting output codes (ECOCs) for incorporating 11 physical parameters to generate spatial grids that reflected land-suitability levels. The probabilistic outputs were derived by calibrating SVM decision values using Platt scaling within the ECOC framework, enabling a reliable estimation of class-wise landsuitability probabilities. The model achieved the highest probability value of 0.9952, with an average of 0.8251; this demonstrated its potential for assessing the consistency of land use/land cover with spatial plans. The model exhibited robust performance and substantial agreement between the predictions and actual data, with an overall accuracy of 88.56% and a kappa index of 0.873. Additionally, the study utilized a land-suitability model and non-weighted overlay relevance matrix to identify discrepancies in Bogor Regency’s spatial plan, quantifying the compliant and noncompliant land areas for each LULC class within specified spatial-plan zones. The evaluation revealed a significant misalignment, with 25–45% of agricultural land uses that included wetland and dryland agriculture, plantations, and inland fish farms being allocated within settlement zones; this indicated a mismatch between spatial plans and land suitability. These findings underscored the importance of evaluating and revising the spatial plan to enhance its alignment with land suitability.

Over the years, urban heat island (UHI) has emerged as a significant contributor to global warming, thereby necessitating considerable attention. Currently, satellite technology is a basic tool for the future – particularly, for its effective and efficient urban analysis. Thus, this study aims to assess the progress of existing satellite-based UHI studies by reviewing scientific publications that were released between 1972 and early 2024. Moreover, we observed that 1991 was a pivotal year, marking the integration of satellite technologies into the development of UHI monitoring and identification systems based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this review methodology examines the UHI phenomenon by focusing on its characteristics based on sensors, algorithms, and accuracy. The results of the systematic review revealed that Landsat and MODIS were the most-deployed sensors for UHI identification and monitoring, while the land surface temperature (LST) indicator and normalized difference vegetation index (NDVI) were the most-deployed algorithms. Regarding accuracy, the integration of satellite sensors and algorithms into UHI studies provides a promising range of accuracies. The review found that the future of satellite-based UHI monitoring is promising, with technological advancements driving the development of effective techniques such as data fusion, gap filling, machine learning (ML), and deep learning. Additionally, Google Earth Engine (GEE) is a cloud-based platform for performing large-scale geospatial analyses, which facilitates the assessments of local, regional, and global-scale UHIs. Finally, the other review findings for future directions indicated that future satellite-based UHI studies will prioritize six crucial points: enhancing data resolution, integrating satellite data with ground-based sensors, artificial intelligence, and ML, climate change modeling, and a global study of UHIs and their impacts.

The extensive generation of waste and intensified geological processes that result from hard coal mining and active operations within mining regions have led to increases in the pollution levels of ecosystems. Most coal-mining wastes contain significant amounts of heavy metals and are, therefore, particularly hazardous to the environment. The soils around waste heaps can be contaminated with various pollutants. This article presents the results of a study of soils that were sampled in the impact zone of the waste heap of the Chervonohradska CPP of the Chervonohrad Mining District. Using statistical methods (including variogram modeling and spatial interpolation), we analyzed the spatial distributions of heavy metals in the affected soil zones. This approach allowed for an enhanced understanding of contamination-dispersion patterns and potential risk areas. The authors collected soil samples from the depth of the biotically active humus-accumulative horizon from the lower tier of the slope of the waste heap at distances of 20 m, 40 m, and 100 m from the spoil tip. We measured the contents of the studied elements in the soil using X-ray fluorescence analysis and assessed the quality of the soil by phytotesting using the Triticum aestivum L. and Lepidium sativum L. test species. It was found that the average concentrations of certain heavy metals in multiple samples exceeded the background values for the region and affected the inhibition of the development and growth of the test objects.

This research concerned the directions of changes in the real estate market in the area of private nursing homes for the elderly. The progressive decline in Poland’s population, the decline in the fertility rate, and demographic projections for the population structure have given rise to considerations about areas of the real estate market that will have to meet the expectations of elderly real estate participants. The growing number of privately owned nursing homes represents a commercial real estate sector that can be viewed as a profitable investment venture. The study examined investment performance indicators for several private nursing homes in Poland; the results indicated relatively high levels of EBIDTA margins for each company.

This study evaluates the preliminary performance of the dual-frequency multiconstellation satellite-based augmentation system (DFMC SBAS) prototype that was deployed in Thailand, focusing on key performance indicators such as positional accuracy and continuity. To this end, real data that was collected from 4, 8, and 12 ground tracking stations in Thailand was used to calculate SBAS corrections for the periods of January 1–7, April 1–7, August 1–7, and December 1–7, 2023. The accuracy of these corrections for single-point positioning was then tested using data from 20 continuously operating reference stations (CORS) in the region. The results showed that the correction data that was derived from the data from the 8 and 12 ground tracking stations significantly improved the efficiency of the single-point positioning, thus meeting the required standards for Category I (CAT-I) aviation operations. This initial assessment provides a solid foundation for the continued development of a fully operational DFMC SBAS that is tailored to Thailand’s specific requirements.

This research concerns the phenological phenomenon of the autumn discolorations of sessile oak leaves as the trees prepare for winter dormancy. Sessile oak trees were categorized into five classes according to the general colors of their crowns: from green to brown. Low-altitude UAV-acquired images from the visible B, G, and R bands were used, compared, and evaluated against the results of several classification methods: those that were carried out in the field, visually based on orthomosaic observations, and four variants of digital classification.The analysis showed that those methods that were based on observer assessments were highly subjective. At the same time, there was also the problem of the reference data to which the results of the individual methods could be referred. It was expected that the analyzed phenomenon of tree-crown discoloration would be better visible in aerial photographs than in field observations; However, visual color classifications using orthomosaics can be too subjective (as has been shown). It is recommended to use supervised digital classification with a careful selection of reference (training) objects.To switch from pixel-classification results to individual tree classifications, a novel approach was adopted in which the class value that was most abundant within the images of each canopy (determined by the supervised classification method selected) could be used. Among the supervised digital-classification methods that were applied, the results that were closest to the classification performed in the field were obtained by using the ML and Fisher algorithms (followed by kNN).

This paper presents the functionality and research possibilities of an application that is based on two concepts: the use of photogrammetric analysis for LiDAR data processing (lidargrammetry), and the assignments of identifiers to cloud points in order to be able to return to the original points after processing without data loss and redundant processing.The research tool has, thus far, been developed for the implementation of two distinct LiDAR data-enhancement processes. The initial approach involves the altimetric transformation of the LiDAR data (a process that is founded on the principles of stereo model deformation theory), and the second process employs lidargrammetry for the purpose of 3D local point-cloud corrections, global changes, or non-rigid transformation. This is achieved by applying blocks of lidargrams and their subsequent matching and adjustments.

This article proposes a methodology for evaluating the applicability of real estate information systems. Despite the fact that such systems have been created by different agencies and for different reasons, all of them should effectively deliver their designed functionalities. These functionalities are determined by the types of users. Real estate information systems have been developed on the assumption that they would be accessible to all citizens; therefore, the applicability of these systems should be examined. In this context, applicability is defined as the degree to which a product, system, or service enables users to achieve their goals in an efficient, effective, and satisfactory manner. A universal methodology for evaluating the applicability of a given system has been proposed and validated in this study. Certain limitations of real estate information systems that could affect their use in land administration procedures were also identified.

Natural disasters and food security challenges frequently impact many countries, including Indonesia. Over the past decade, the development of remote-sensing technology (particularly, synthetic aperture radar [SAR]) has garnered the attention of policymakers. Its ability to penetrate clouds and rain and data-acquisition techniques unaffected by time (day or night) provide advantages for describing the equatorial region. The application of SAR technology in Indonesia has progressed significantly. However, an important question has arisen: to what extent is the impact of using SAR data? Most SAR data in Indonesia is still limited to academic circles. To address this question comprehensively, this research examines the extent to which studies utilize SAR data – particularly, in global publications. The scope of this research was limited to articles published between 2013 and early 2025 that utilized SAR as the primary or secondary methods. The gap between the numerous studies on SAR technology and its significant impact on various government policies was quantitatively analyzed. In conclusion, this research underscored the need for a more nuanced approach toward integrating SAR technology into policymaking in Indonesia. This study serves as a critical reflection on the current state of SAR research in Indonesia, calling for a more concerted effort to bridge the gap between technical studies and actionable policy formulation.

In recent years, global navigation satellite systems (GNSSs) have emerged as a prominent technology for geolocation applications and services in urban settings. Urban environments should also be classified under difficult situations. Densely populated metropolitan areas such as urban centers obstruct the receipt of GNSS signals; these obstacles often result in the congestion of line-of-sight (LOS) signals and give rise to the receipt of diffracted or reflected echoes (often known as the multipath phenomenon). PPK (post-processing kinematic) is a GNSS data-processing method that achieves high-accuracy positioning by correcting errors in raw positioning data. Post-processing is widely used in applications that require precise geospatial information, such as surveying, mapping, and UAV operations. This research aims to evaluate the accuracy of the PPK application method in urban areas. For this aim, surveys were carried out in Turgutlu’s province of Manisa on July 15, 2020, in Türkiye. The analysis compared the PPK surveys’ results with those that were obtained from static surveys. PPK is very effective in difficult situations, but we were likely to encounter certain accuracy problems. Nevertheless, it is worth noting that achieving urban surveys with an accuracy from ±1 cm to ±2 cm may not always be feasible due to the challenging circumstances that might result in moresignificant inaccuracies from ±10 cm to ±100 cm for both the horizontal and vertical components.