Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland

Karsznia, Izabela; Przychodzeń, Marta; Sielicka, Karolina

doi:10.1080/10106049.2018.1533591

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Currently, full automation of this process is one of the main goals of Polish NMA [94]. However, as it was recently stated in [95], it is not possible to perform automatic generalization of BDOT10k, using out-of-the-box software. In the Netherlands, a decade ago the authorities wanted to develop a new large-scale topographic standard of a vario-scale database-IMGeo.…”

Section: Research Question 3-how To Properly Design Spatial Databasesmentioning

confidence: 99%

Automatic (Tactile) Map Generation—A Systematic Literature Review

Wabínski¹,

Mościcka²

2019

IJGI

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This paper presents a systematic literature review that reflects the current state of research in the field of algorithms and models for map generalization, the existing solutions for automatic (tactile) map generation, as well as good practices for designing spatial databases for the purposes of automatic map development. A total number of over 500 primary studies were screened in order to identify the most relevant research on automatic (tactile) map generation from the last decade. The reviewed papers revealed many existing solutions in the field of automatic map production, as well as algorithms (e.g., Douglas–Peucker, Visvalingam–Whyatt) and models (e.g., GAEL, CartACom) for data generalization that might be used to transform traditional spatial data into the haptic form, suitable for blind and visually impaired people. However, it turns out that a comprehensive solution for automatic tactile map generation does not exist.

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Section: Research Question 3-how To Properly Design Spatial Databasesmentioning

confidence: 99%

Automatic (Tactile) Map Generation—A Systematic Literature Review

Wabínski¹,

Mościcka²

2019

IJGI

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“…The former mainly combines coordinate conversion, map projection, and alignment methods when the inconsistency detection is for same-scale geospatial data [18][19][20][21]. Meanwhile, the latter inconsistency detection for cross-scale geographic objects is fulfilled through spatial or knowledge databases [22,23]. Moreover, it is also quite typical to quantify and detect inconsistencies or changes in geospatial data through object matching [24] or map algebra [25].…”

Section: Introductionmentioning

confidence: 99%

Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation

et al. 2023

IJGI

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The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can be directly available for multi-level tile images in a pixel representation form. The proposed cross-layer raster tile map rendering method (CRTMRM) consists of four primary aspects: geographic object separation, consistency rendering rules, data scaling and derivation with super-pixel segmentation, and inconsistency detection. The scale transformation strategy with the super-pixel attempts to obtain a simplified representation. Taking the scale lifespan variation and geometric consistency rules into account, the inconsistency detection of tile maps is conducted between temporal versions, multi-sources, and different scales through actual and derived data overlay analysis. The experiment focuses on features of cross-layer water or vegetation areas with Level 9 to Level 14 in Baidu Maps, Amap, and Google Maps. This method is able to serve as a basis for massive unstructured web map data inconsistency detection and support intelligent web map rendering.

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“…The key and core step in the generalization processing is road selection, which aims to reduce the level of detail in a road network by retaining important roads and eliminating less important roads while also maintaining the main road spatial characteristics and network structure [4][5][6], as well as essential semantic, topological, and geometrical properties [7].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Generalization Method for a Dense Road Network Area Considering Spatial Structural Features as Constraints

Yin

Wu³

et al. 2022

IJGI

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Road networks are the skeletal elements of topographic maps at different scales, and road selection is a prerequisite for implementing continuous multiscale spatial representations of road networks. The mesh-based approach is a common, advanced and powerful method for road selection in dense road areas in which small meshes are removed and road segments with the least importance in each mesh are eliminated. However, small meshes in a map can be classified into two types: aggregated small meshes and isolated small meshes. The number of the former is small, and that of the latter is large. Existing methods are generally applicable for the latter, and some or even most spatial characteristics will be lost when they are applied to the former; as a result, the road selection quality will be affected. Therefore, as a supplement to the mesh-based selection method, this paper proposed an automatic generalization method of dense road network areas (areas formed by aggregated small meshes) considering spatial structural features as constraints. First, the aggregated areas of small meshes were identified based on the number of adjoining small meshes, and the boundaries of aggregated areas are extracted and used as hard constraints during mesh elimination. Second, the starting meshes were redefined by simultaneously considering the edge features and mesh density of small meshes, and an ordinal elimination algorithm was proposed to eliminate the meshes in the stroke connection direction. Third, road selection was implemented by identifying the starting meshes and sequentially processing the related mesh pairs. This iterative process continued until all mesh densities of the newly formed meshes are beyond the threshold or the problem becomes a simple elimination problem involving two adjoining small meshes or one isolated small mesh. Finally, a 1:10,000 standard topographic road map for Jiangsu Province, China, was used for validation. The experimental results showed that in the aggregated areas with two small meshes, 31% of the areas obtained the same selection results by using the mesh-based method and the proposed method, and the remaining 69% obtained a more compact result with the proposed method. Moreover, for all aggregated areas with more than two small meshes, the spatial distribution structure of small meshes was preserved better by the proposed method.

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Methodology of the automatic generalization of buildings, road networks, forests and surface waters: a case study based on the Topographic Objects Database in Poland

Cited by 4 publications

References 23 publications

Automatic (Tactile) Map Generation—A Systematic Literature Review

Automatic (Tactile) Map Generation—A Systematic Literature Review

Inconsistency Detection in Cross-Layer Tile Maps with Super-Pixel Segmentation

An Automatic Generalization Method for a Dense Road Network Area Considering Spatial Structural Features as Constraints

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