The Reconnection of Contour Lines from Scanned Color Images of Topographical Maps Based on GPU Implementation

Song, Jianfeng; Wang, Panfeng; Miao, Qiguang; Liu, Ruyi; Huang, Bormin

doi:10.1109/jstars.2016.2580903

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…La reconexión de líneas de contorno son elementos gráficos que permiten la caracterización 3D de terrenos en mapas de 2D, por lo que en (Song et al, 2017) se expone un método efectivo basado en GPU, se logró in mejoramiento de velocidad de 1360x con resultados idénticos a los obtenidos en CPU.…”

Section: Información Geográficaunclassified

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

Aguilera

Aceves

Argüelles

et al. 2018

CULCyT

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RESUMENEL procesamiento de imágenes es una herramienta de gran utilidad en diversas aplicaciones como video vigilancia, reconstrucción de imágenes, información geográfica y médica. Sin embargo, estas aplicaciones requieren una gran demanda computacional para ser llevadas a cabo en el menor tiempo posible, aún y que se desarrollan nuevos algoritmos, suelen ser restrictivos para implementarse en tiempo real en sistemas que solo se basan en CPU. Afortunadamente, estos algoritmos pueden ser analizados para llevarse a cabo en plataformas de cómputo paralelo, como las GPU-CUDA. En este trabajo se analizan diferentes revistas de la IEEE desde el 2013, donde se paralelizaron algoritmos con estas aplicaciones y se implementaron con ayuda de GPU´s. Se destaca que el uso de esta herramienta ha ido en crecimiento en el ámbito científico, en diferentes ramas de la ciencia.

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Section: Información Geográficaunclassified

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

Aguilera

Aceves

Argüelles

et al. 2018

CULCyT

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“…(3) The filtered LiDAR data set is input, which can be taken as ground check points, and the elevations of the check points are estimated by linear interpolation using the triangulation network constructed in Section 3. 3 Step 2. The elevation deviations between the interpolated points and the check points are then calculated.…”

Section: Accuracy Assessment Of Contour Lines By the Statistical-basementioning

confidence: 99%

“…Traditionally, contour lines can be generated by photogrammetric methods [1][2][3]. However, despite their common use, these methods are quite expensive and low efficiency.…”

Section: Introductionmentioning

confidence: 99%

High-Quality Contour Line Generation from LiDAR Point Clouds for the Area of Forests

Wang¹,

Ma²,

Cheng³

et al. 2018

Preprint

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“…Many methods have been developed for extracting different types of entities and information from map images. Examples include the methods for extracting textual labels (Leyk et al 2006, Pezeshk and Tutwiler 2011, Xu et al 2016, road segments (Callier andSaito 2012, Chiang andKnoblock 2013), road intersections (Chiang et al 2009, Saeedimoghaddam andStepinski 2019), contour lines (Liu et al 2016, Song et al 2016, built-up areas (Muhs et al 2016), and map symbols (Miao et al 2016). In addition, there also exist software tools, such as ArcScan from ArcGIS and Polygonize from QGIS, that can be used for extracting vector entities from scanned map images.…”

Section: Introductionmentioning

confidence: 99%

Aligning geographic entities from historical maps for building knowledge graphs

Sun

Song

et al. 2020

International Journal of Geographical Information Science

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Historical maps contain rich geographic information about the past of a region. They are sometimes the only source of information before the availability of digital maps. Despite their valuable content, it is often challenging to access and use the information in historical maps, due to their forms of paper-based maps or scanned images. It is even more timeconsuming and labor-intensive to conduct an analysis that requires a synthesis of the information from multiple historical maps. To facilitate the use of the geographic information contained in historical maps, one way is to build a geographic knowledge graph (GKG) from them. This paper proposes a general workflow for completing one important step of building such a GKG, namely aligning the same geographic entities from different maps. We present this workflow and the related methods for implementation, and systematically evaluate their performances using two different datasets of historical maps. The evaluation results show that machine learning and deep learning models for matching place names are sensitive to the thresholds learned from the training data, and a combination of measures based on string similarity, spatial distance, and approximate topological relation achieves the best performance with an average F-score of 0.89.

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The Reconnection of Contour Lines from Scanned Color Images of Topographical Maps Based on GPU Implementation

Cited by 8 publications

References 16 publications

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

Utilización de GPU-CUDA en el Procesamiento Digital de Imágenes

High-Quality Contour Line Generation from LiDAR Point Clouds for the Area of Forests

Aligning geographic entities from historical maps for building knowledge graphs

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