Figure 3 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Source publication
Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological m...
Context in source publication
Citations
... The limitation of using 'overview' maps for detailed analysis is evident in Fig. 2a, where the peatland area was entirely omitted due to polygon map generalization. This process, commonly employed to enhance polygon map legibility at small scales, involves the elimination of certain small polygon features, smoothing of contours, and aggregation of closely spaced features (Smirnoff et al., 2012;Sayidov et al., 2020). While 'overview' QD maps (scales <1:100,000) are acceptable for national-level visualization, their use in other contexts such as GIS analyses, detailed planning, and decisionmaking requires caution (The Geological Survey of Sweden, 2024b). ...
... The limitation of using 'overview' maps for detailed analysis is evident in Fig. 2a, where the peatland area was entirely omitted due to polygon map generalization. This process, commonly employed to enhance polygon map legibility at small scales, involves the elimination of certain small polygon features, smoothing of contours, and aggregation of closely spaced features (Smirnoff et al., 2012;Sayidov et al., 2020). While 'overview' QD maps (scales <1:100,000) are acceptable for national-level visualization, their use in other contexts such as GIS analyses, detailed planning, and decisionmaking requires caution (The Geological Survey of Sweden, 2024b). ...
... Automated geological map generalization could be divided into two main research approaches, vector-based generalization or an integration of vector-and raster-based generalization. For the former one, Downs and Mackaness (2002), Steiniger and Weibel (2005), and Sayidov et al. (2020) provide some examples. While an integrated approach using vector-and raster-based generalization was addressed by Smirnoff et al. (2008), Smirnoff et al. (2012), and Schuff (2019). ...
... Comparison of Fig. 3b and 3a suggests that structural orientation information interpreted from aerial imagery (Fig. 3b) would have less variance than that obtained from outcrop-scale Length Scale as a Fourth Spatial Dimension in Geology and Geophysics measurement ( Fig. 3a, stereonet in Fig. 3b). This is also an example of generalization (Downs & Mackaness, 2002;Jiang et al., 2013;Sayidov et al., 2020;Stewart, 2018). ...
Three-dimensional coordinate systems define the location of zero-dimensional points. Higher dimensional spatial objects such as lines and planes become apparent from sets of adjacent points. Any point defined in three-dimensional space within a sedimentary basin could be simultaneously located on the surface of a sand grain and a bedding plane in a kilometer-scale fold, and structure at any other scale. Therefore, like many other aspects of geology and geophysics, structural geology is multiscale which is why filtering, smoothing, scale bars and the like are prerequisites for any kind of geological mapping and outcrop imagery. It is argued here that the association of points with higher-dimensional objects that form the spatial building blocks of geology singles out length scale as a geometrical parameter that is fundamental to the definition of an object. Properties such as curvature, porosity, color and so on can only be attributed after location and scale are defined. It is proposed that length scale be treated as a fourth spatial dimension in geological and geophysical applications, on an equal footing to the three spatial coordinate dimensions. The utility of adopting a four spatial dimension approach in geoscience is that it forces length scale to be explicitly specified in descriptions where it is routinely omitted, for example, measurements of structural orientation where a scale parameter greatly simplifies later mapmaking and model building. Taken together with geological time, it is convenient to define geological and geophysical structure in five spacetime dimensions.
... Some morpho-structures (trench and doubled ridge) of which the only axis is reported in TGM can be decomposed in different elements in OBM (scarps, depressions and counterscarps). In detail, the set of ID polygons 1, 2 etc. can be decomposed in its elementary polygons 1a, 1b etc. and 2a, 2b, etc. in a multi-scale-related combination [4,[20][21][22]. ...
The research reports the use of a new method of geomorphological mapping in GIS environ-ment, using a full coverage object based method, following the guidelines of the new geomor-phological legend proposed by ISPRA-AIGEO-CNG. This methodology is applied to a tributary valley of the Germanasca Valley shaped into calcschist and greenschist of the Piedmont Zone (Penninic Domain, Western Alps). The investigated sector is extensively affected by Deep-Seated Gravitational Slope Deformation (DSGSD) that strongly influences the geological setting and the geomorphological features of the area. The mapping of these gravitational landforms in a traditional way creates some difficulties essentially connected to the high density of infor-mation in the same site and the impossibility of specify the relationships from different ele-ments. The use of full coverage object based method is instead, advantageous in mapping of gravitational evidence. In detail, it allows to represent various landforms in the same sector and their relationship, specifying the size of landforms, also with possibility of multiscale representa-tion in GIS environment and it’s updating in the time with progress of knowledge. This research confirms that the use of the full coverage object based method allows to better mapping the ge-omorphological features of DSGSD evidence, compared to the classical representation.
... This can be rephrased as classification or generalization problem and thus could motivate new application areas and a fresh view on cartographic generalization methods, for example by applying constraint-based generalization (Sayidov et al., 2020) to prepare input data for the tiling process. Another area of traditional knowledge to tie in with are developments in the AEC area, such as the derivation of architectural drawings from 3D data and more recent reinterpretations for interactive media (Gotlib et al., 2020) or applications for indoor maps via building floor plans in GIS context (Konde et al., 2018, for example). ...
Current attempts to integrate gaming, geospatial and AEC technologies are driven by increased hardware capabilities and target virtual worlds as close to reality as possible. In this paper we pursue a converse destination and populate low-tech virtual worlds from geospatial data, building and city models, specifically for retro gaming engines in spatial chat tools. We consider an island, a building and a city district scenario and populate these scenarios from OSM, IFC, and CityGML data sources. The derived worlds are targeting use cases in business, educational and recreational settings. Based on a prototypical implementation, we study the feasibility and limitations of good quality retro gaming map generation with automatic means from existing data sets, but also the potential of such worlds for Green IT, accessibility and inspiration of new user groups. We describe the algorithms and processes to generate the maps and outline the concept for a user survey. Beyond that, by discussing map generation techniques from classical gaming in context of and comparison with geospatial and AEC practices, this paper contributes a retrospective of early gaming techniques that might be both entertaining and informative for current development practice.
... The last theme emerging from this special issue identifies new cartography domains where research on generalization is just beginning or has been neglected. Sayidov et al. [6] examine constraint-based generalization of geologic maps, which stand out in terms of their complex pattern of areal partitions that vary in size, shape and cluster patterns. They show that a combination of constraints on area, separation distances, and granularity can successfully guide elimination, enlargement, aggregation and displacement of geologic units. ...
Generalization of geospatial data is a cornerstone of cartography, a sequence of often unnoticed operations that lays the foundation of visual communication [...]
Quantitatively expressing spatial similarity is a prerequisite of using it as constraints in map generalization; nevertheless, it has not yet been well solved. To fill the gap, this study firstly proposes the methods for calculating spatial similarity degree between an individual object (or an object group) at a larger scale and its generalized counterpart at a smaller scale, and forms formulae for calculating spatial similarity degree using change of map scale and vice versa. Based on the quantification methods of spatial similarity, potential use of spatial similarity as constraints in map generalization are given.
