An important problem associated with the aerial mapping of the seabed is the precise classification of point clouds characterizing the water surface, bottom, and bottom objects. This study aimed to improve the accuracy of classification by addressing the asymmetric amount of data representing these three groups. A total of 53 Synthetic Minority Oversampling Technique (SMOTE) algorithms were adjusted and evaluated to balance the amount of data. The prepared data set was used to train the Multi-Layer Perceptron (MLP) neural network used for classifying the point cloud. Data balancing contributed to significantly increasing the accuracy of classification. The best overall classification accuracy achieved varied from 95.8% to 97.0%, depending on the oversampling algorithm used, and was significantly better than the classification accuracy obtained for unbalanced data and data with downsampling (89.6% and 93.5%, respectively). Some of the algorithms allow for 10% increased detection of points on the objects compared to unbalanced data or data with simple downsampling. The results suggest that the use of selected oversampling algorithms can aid in improving the point cloud classification and making the airborne laser bathymetry technique more appropriate for seabed mapping.

Plastic pollution is becoming an increasingly serious threat to the natural environment. Macroplastics, primarily polyethylene films, pose significant ecological and economic risks, particularly in the agricultural sector. Effective monitoring of their presence is necessary to evaluate the effectiveness of mitigation measures. Conventional techniques for identifying environmental contaminants, based on field studies, are often time-consuming and limited in scope. In response to these challenges, a study was conducted with the primary aim of utilizing unmanned aerial vehicles (UAVs), multispectral cameras, and classification tools to monitor macroplastic pollution. The model object for the study was an industrial compost pile. The performance of four object-oriented classifiers—Random Forest, k-Nearest Neighbor (k-NN), Maximum Likelihood, and Minimum Distance—was evaluated to effectively identify waste contamination. The best results were achieved with the k-NN classifier, which recorded a Matthews Correlation Coefficient (MCC) of 0.641 and an accuracy (ACC) of 0.891. The applied classifier identified a total 37.35% of the studied compost pile’s surface as contamination of plastic. The results of the study show that UAV technology, combined with multispectral imaging, can serve as an effective and relatively cost-efficient tool for monitoring macroplastic pollution in the environment.

Landscaping has a major impact on the natural phenomena that occur on the surface of the Earth. Directive 2000/60/EC of the European Parliament and of the Council of Europe (23/10/2000) prescribes an obligation of sustainable management of water resources, including by protecting them against...

While parameters such as slope and aspect are routinely considered in solar radiation modelling, the role of terrain or surface roughness remains underexplored, with no universally accepted method for its calculation. This study compares several approaches to quantifying terrain or surface roughness in several geographical information system (GIS) environments (ArcGIS, QGIS, WhiteboxTools, and SAGA GIS) and introduces local fractal dimension, computed using a custom Python script, as an additional metric. The aim is to evaluate the influence of surface roughness on potential solar radiation modelling and to examine its relationship with other terrain parameters. The analysis is based on case studies from both a rugged alpine environment in the Tatra Mountains (Tichá and Kôprová dolina (valleys), Kriváň peak; 944–2467 m a.s.l.) and an urban environment (the city of Poprad, near the High Tatras, Slovakia). The results demonstrate that surface roughness can significantly affect potential solar radiation modelling in areas with high surface variability. The findings are applicable not only to solar radiation studies, but also to other fields of spatial modelling, where incorporating surface roughness can improve the accuracy and robustness of spatial analyses and predictions.

Rapid conversion of agricultural land into low-density housing reshapes local water balances at a time when climate change is intensifying hydrological extremes in Central Europe. This study evaluates the rooftop rainwater harvesting (RWH) potential of a newly urbanized housing estate in Rokietnica (Greater Poland Voivodeship, Poland) using a publicly available true orthophoto (5 cm ground sampling distance) as a reliable geometric data source. Roof footprints of 41 single-family buildings (5305 m² in total) were manually vectorized in QGIS and combined with monthly precipitation recorded at the nearest meteorological station (Złotniki) in a simplified volumetric model (V = A × R × C, with C = 0.95). To place the single reference year (2021) in its climatic context, the full 72-year precipitation record (1952–2023) was analyzed using the Mann–Kendall test and Sen’s slope estimator. The results indicate a harvestable volume of approximately 2642 m³ in 2021 (53–73 m³ per building; mean 64 m³), with May and August jointly accounting for almost one-third of the annual total. Annual precipitation at Złotniki exhibits a statistically significant increasing trend (+1.29 mm yr⁻¹; p = 0.028) concentrated in winter and early spring, while summer totals remain trendless but highly variable. A monthly storage simulation driven by the recent 30-year record (1994–2023) shows that summer garden irrigation, the dominant practical application of harvested rainwater in Polish households, is considerably harder to meet from the roof alone than year-round indoor non-potable uses: a 5 m³ tank covers approximately 73% of the seasonal demand of a 100 m² garden plot, while a 3–5 m³ tank would theoretically secure 91–98% of toilet-flushing demand for a four-person household. Under Poland’s national rainwater co-financing scheme (formerly “Moja Woda,” active 2020–2024, succeeded by “Mikroretencja” from June 2026), simple payback periods of roughly 3–7 years make household installations financially defensible. The findings suggest that true orthophotos enable rapid, low-cost RWH assessments in dynamically developing suburbs, and that storage sizing should anticipate the ongoing seasonal redistribution of precipitation under climate change. To our knowledge, this is the first study to combine free national true-orthophoto imagery, a 72-year Mann–Kendall trend analysis, and a subsidy-linked storage-reliability simulation within a single suburban rainwater-harvesting assessment.