This study proposes an automatic building outline regularization approach (ABORE) that produces two-dimensional orthogonal building boundaries from irregular building footprint polygons with building edge points using raw LiDAR point cloud data. The proposed approach was evaluated on the combinations of single, double, and multiple buildings from seven LiDAR datasets with different point density. The accuracy of the building edge geometries regularized by the ABORE method was compared with the reference data without requiring any additional data. Test results showed that the mean completeness (Cp), correctness (Cr), and quality (Q) values for single buildings were estimated as 98.96, 99.32, 98.30% and for multiple buildings as 99.55, 99.74, 99.29%, respectively. These results indicate superior performance (by about 5% F-score improvement) in comparison to state-of-the-art methods with a higher correctness rate in building boundary regularization. ABORE was also found effective in the delineation of complex buildings with many corners and short edges.

The extraction of UAV building point cloud contour points is the basis for the expression of a three-dimensional lightweight building outline. Previous unmanned aerial vehicle (UAV) building point cloud contour extraction methods have mainly focused on the expression of the roof contour, but did not extract the wall contour. In view of this, an algorithm based on the geometric features of the neighborhood points of the region-growing clustering fusion surface is proposed to extract the boundary points of the UAV building point cloud. Firstly, the region growth plane is fused to obtain a more accurate segmentation plane. Then, the neighboring points are projected onto the neighborhood plane and a vector between the object point and neighborhood point is constructed. Finally, the azimuth of each vector is calculated, and the boundary points of each segmented plane are extracted according to the difference in adjacent azimuths. Experiment results show that the best boundary points can be extracted when the number of adjacent points is 24 and the difference in adjacent azimuths is 120. The proposed method is superior to other methods in the contour extraction of UAV buildings point clouds. Moreover, it can extract not only the building roof contour points, but also the wall contour points, including the window contour points.

The historical structures are one of the cornerstones of cultural heritage, shedding light on humanity's past. With their artistic and architectural values, they serve as significant monuments reflecting the lifestyles, beliefs, and technological advancements of past civilizations. Therefore, the aim of this study is to identify and document the deteriorations occurred after the restoration of Ishak Pasha Palace located in the Doğubayazıt district of Ağrı province, using a UAV (Unmanned Aerial Vehicle) obtained 3D model and orthophotos. Within the scope of the study, three main types of deterioration arising from physical, chemical, and biological effects on Ishak Pasha Palace were investigated using the 3D model. Subsequently, all obtained results were visualized in detail. Identification of these types of deterioration is crucial for the preservation and restoration of historical structures, enabling appropriate interventions to be made. Regular maintenance and conservation of architectural structures are of critical importance in the transmission of cultural heritage to future generations. Ultimately, it is believed that this study will contribute to the widespread adoption of UAV usage in the detection of deteriorations observed in historical structures.

Soil salinization poses significant challenges to agricultural productivity, food security, ecological balance, and environmental sustainability. Concerning monitoring and mapping soil salinity, Remote Sensing (RS) has emerged as a pivotal approach, offering cost-effective and scalable solutions compared to conventional laboratory methods. This study aims to present a detailed bibliometric analysis of research articles on soil salinity monitoring and mapping using remote sensing technologies published over the past three decades (1988–2023), leveraging the SCOPUS database and tools like VOSviewer. The analysis examines 566 publications to identify trends, advancements, and research gaps, and it reveals an exponential growth in research outputs, with 82% of studies published in the last decade and 60% in the past five years. Key findings include the dominance of geospatial technologies such as optical and radar remote sensing, as well as emerging methodologies like machine learning algorithms (e.g., Random Forest, Support Vector Machines) and hyperspectral imaging. Collaborative research, particularly among international authors, has significantly contributed to the advancement of the field. Geospatial techniques are frequently employed for salinity monitoring, with research hotspots identified in regions like the Yellow River Delta (China), Bangladesh Coastal Zone, Ebinur Lake, Xinjiang, and Aral Sea. China leads in publication count, while countries like Australia and the Netherlands exhibit higher citation impacts. On the other hand, key findings highlight the utility of remote sensing for addressing soil salinity’s spatial and temporal dynamics, revealing significant gaps in underrepresented regions such as sub-Saharan Africa and South America. Future research directions include improving multi-source data integration, exploring advanced algorithms for big data analytics, and addressing the socioeconomic dimensions of soil salinization. This comprehensive analysis provides a roadmap for leveraging remote sensing technologies to mitigate the impacts of soil salinity on sustainable agriculture and environmental resilience.