![]() ![]() With the widespread accessibility of detailed DEMs, this principle is often violated, and the data are used for mapping at scales far smaller than what is appropriate. Specifically, at small scales, intensive generalization is needed, which is also true for elevation data. ![]() At the same time, geographic problem solving is conducted in a wide variety of scales, and the data used for mapping should have the corresponding level of detail. Recent advancements in the coverage, quality, and resolution of global DEMs facilitate the overall improvement of the detail and reliability of terrain-related research. DEMs are widely used in mapping, most commonly in the form of contours, hypsometric tints, and hill shading. One of the key applications of digital elevation models (DEMs) is cartographic relief presentation. In addition to the simulation experiment, two case studies with map vectorization and trajectory filling illustrate the application prospects of our model. Qualitative and quantitative evaluations show that our model can fill missing points with high perceptual quality and adaptively handle a range of gaps. The experiments generated gaps of random sizes at random locations along with the polyline samples. The proposed model was applied to contour data for validation. Contour interval generator#Specifically, the generator can compute the content of the entire polyline sample globally and produce a plausible prediction for local gaps. The model is trained to generate the contents of missing polylines of different sizes and shapes conditioned on the contexts. In this paper, we propose an effective framework for vector-structured polyline completion using a generative model. Recent advances in deep learning have shown promise in filling holes in images with semantically plausible and context-aware details. Contour interval how to#An essential concern is how to fill spatial data gaps (missing data), such as for cartographic polylines. Geospatial studies must address spatial data quality, especially in data-driven research. The minimum carcinogenic risk is characteristic of the highways and TRR with predominance of nonstop traffic. The carcinogenic risk for adults is the highest in courtyard areas in the south, southwest, northwest, and center of Moscow. The main BaP exposure pathway is oral via ingestion (> 90% of the total BaP intake). Public health risks from exposure to BaP-contaminated road dust particles were assessed using the US EPA methodology. The accumulation of BaP depends on the parameters of street canyons formed by buildings along the roads: in short canyons ( 20 m. In the city center, the BaP content in the dust of courtyards reaches 1.02 mg/kg (MPC excess by 51 times). The most polluted territories are large roads (0.29 mg/kg, excess of the maximum permissible concentration (MPC) in soils by 14 times) and parking lots in the courtyards (0.37 mg/kg, MPC excess by 19 times). The average BaP content in road dust is 0.26 mg/kg, which is 53 times higher than the BaP content in the background topsoils (Umbric Albeluvisols) of the Moscow Meshchera lowland, 50 km east of the city. For the first time, the accumulation of BaP in road dust on different types of Moscow roads has been determined. Benzopyrene (BaP) is one of the priority pollutants in the urban environment. ![]()
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