Mapping Road Based on Multiple Features and B-GVF Snake

Wang, Fengping; Li, Ying

doi:10.1142/s0218001420500354

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…The application of the snake algorithm is varied and has been used for segmentation, edge detection, shape modelling, and tracking motion. The active contours or snakes move under the impact of forces (both internal and external) from the curve and image data, respectively [23]. Cubic B-spline with fewer state variables provides more economical recognition of snake and are piecewise polynomial functions.…”

Section: B-spline Snakementioning

confidence: 99%

Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

Munawar

Hammad

Waller

et al. 2023

Hum-Cent Intell Syst

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Road network detection is critical to enhance disaster response and detecting a safe evacuation route. Due to expanding computational capacity, road extraction from aerial imagery has been investigated extensively in the literature, specifically in the last decade. Previous studies have mainly proposed methods based on pixel classification or image segmentation as road/non-road images, such as thresholding, edge-based segmentation, k-means clustering, histogram-based segmentation, etc. However, these methods have limitations of over-segmentation, sensitivity to noise, and distortion in images. This study considers the case study of Hawkesbury Nepean valley, NSW, Australia, which is prone to flood and has been selected for road network extraction. For road area extraction, the application of semantic segmentation along with residual learning and U-Net is suggested. Public road datasets were used for training and testing purposes. The study suggested a framework to train and test datasets with the application of the deep ResUnet architecture. Based on maximal similarity, the regions were merged, and the road network was extracted with the B-snake algorithm application. The proposed framework (baseline + region merging + B-snake) improved performance when evaluated on the synthetically modified dataset. It was evident that in comparison with the baseline, region merging and addition of the B-snake algorithm improved significantly, achieving a value of 0.92% for precision and 0.89% for recall.

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Section: B-spline Snakementioning

confidence: 99%

Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

Munawar

Hammad

Waller

et al. 2023

Hum-Cent Intell Syst

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“…Diverse road types, such as paved, unpaved, forest, or desert roads, have unique characteristics and require different mapping approaches 28 , 29 . The complexity increases when attempting to differentiate and accurately represent various road surfaces and terrains 30 . This requires sophisticated data processing methods to handle the large amount of data and numerous vertex points involved.…”

Section: Introductionmentioning

confidence: 99%

Mapping roadless areas in regions with contrasting human footprint

Hoffmann,

Ostapowicz,

Bartoń

et al. 2024

Sci Rep

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In an increasingly human- and road-dominated world, the preservation of functional ecosystems has become highly relevant. While the negative ecological impacts of roads on ecosystems are numerous and well documented, roadless areas have been proposed as proxy for functional ecosystems. However, their potential remains underexplored, partly due to the incomplete mapping of roads. We assessed the accuracy of roadless areas identification using freely available road-data in two regions with contrasting levels of anthropogenic influence: boreal Canada and temperate Central Europe (Poland, Slovakia, Czechia, and Hungary). Within randomly selected circular plots (per region and country), we visually examined the completeness of road mapping using OpenStreetMap 2020 and assessed whether human influences affect mapping quality using four variables. In boreal Canada, roads were completely mapped in 3% of the plots, compared to 40% in Central Europe. Lower Human Footprint Index and road density values were related to greater incompleteness in road mapping. Roadless areas, defined as areas at least 1 km away from any road, covered 85% of the surface in boreal Canada (mean size ± s.d. = 272 ± 12,197 km2), compared to only 0.4% in temperate Central Europe (mean size ± s.d. = 0.6 ± 3.1 km2). By visually interpreting and manually adding unmapped roads in 30 randomly selected roadless areas from each study country, we observed a similar reduction in roadless surface in both Canada and Central Europe (27% vs 28%) when all roads were included. This study highlights the urgent need for improved road mapping techniques to support research on roadless areas as conservation targets and surrogates of functional ecosystems.

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“…Due to different data collection methods and time, the spatial location of the same road section is quite different. Direct road network data matching will not only lead to large errors but also reduce the running speed of the algorithm [17,18]. Hence, a rough matching strategy is carried out to estimate the changing state of R ′ (N ′ , A ′ ), and then different fine-matching strategies are selected to improve the matching efficiency.…”

Section: Rough Matching Based On Distance Proximitymentioning

confidence: 99%

Studying a matching method combining distance proximity and buffer constraints

Wang

2022

Applied Mathematics and Nonlinear Sciences

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In order to achieve fast matching of global navigation satellite system (GNSS) track data and vector road network data, a rough-to-fine matching method combining distance proximity and buffer constraints is studied. The GNSS track data is preprocessed: A rasterisation method is used to eliminate the low-quality track data and obtain the binary graph; then, the road centrelines are obtained by a skeleton extraction method; the thinning data is converted into line segments or closed lines starting from nodes by the boundary tracking method, which is stored in the vector form. Finally, the boundary tracking method is used to convert the thinning data into closed lines and store them in the vector form. A two-layer rough-to-fine matching strategy is applied: First, the changing state of the road arc is determined according to the rough matching strategy of road segment distance. Next, according to the buffer constraint criterion, a rectangular or circular buffer is selected to achieve precise matching of road network data. The results show that the proposed method has good matching effect, which is especially suitable for working with more GNSS track data.

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Mapping Road Based on Multiple Features and B-GVF Snake

Cited by 4 publications

References 19 publications

Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

Road Network Detection from Aerial Imagery of Urban Areas Using Deep ResUNet in Combination with the B-snake Algorithm

Mapping roadless areas in regions with contrasting human footprint

Studying a matching method combining distance proximity and buffer constraints

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