Cartographic generalization is a cartography which selects, simplifies and generalizes objects in map according to the cartographic purpose, map scale and geographical features of the generalized ...area. This process creates a new map which should keep the basic geographical features of the generalized area. In map and GIS, the basic geographical features of an area are mainly reflected by spatial objects' shape, spatial distribution density, their distance relations, direction relations and topology relations. Undoubtedly, whether the generalized objects are greatly similar to the original objects in shape, distance, orientation, topology and distribution density is one of important indexes in result evaluation of cartographic generalization. Usually these quality indexes are evaluated by cartographers with their oculars. Obviously this method is very inefficient and the obtained result is subjective and qualitative. Considering the special characteristics of cartographic generalization, this work divides spatial similarity into shape similarity, distance similarity, orientation similarity, topology similarity and distribution density similarity and presents the methods to measure these similarities. Then we use these methods to evaluate whether the result of cartographic generalization keeps the basic geographical features of the generalized area. Many experiments show that the application of spatial similarity measure in the result evaluation of cartographic generalization is reasonable. In addition, it can promote the result evaluation of cartographic generalization to become automatic and quantificational.
Because web and wireless mobile users of geographical information systems require information that is directly relevant to the specific task in which they are engaged, Traditional generalization ...techniques produce maps that are general-purpose presents a new set of challenges with the growth of web and wireless of geographical information systems. Therefore, a possible solution to the problem relies on applying task-oriented generalization techniques including simplification adaptive to the diverse requirements for users of web or wireless mobile GIS. In this paper, the level of detail in the generated web mapping is adapted by progressive generalization algorithm, which remains or reduces online web map description data according to the geometric and semantic of the features represented. At the same time, we research topological checking method for web mapping to avoid topological conflicts. Based on this, we generate web mapping on demand and prove effective generalization methods by concrete experiments.
Heuristics are often used to solve complex problems. Indeed, such problem-specific knowledge, when pertinent, helps to efficiency find good solutions to complex problems. Unfortunately, acquiring and ...maintaining a heuristic set can be fastidious. In order to face this problem, an approach consists in revising the heuristic sets by means of experiments. In this paper, we are interested in a specific revision method of this type based on the exploration of the heuristic space. The principle of this method is to revise the heuristic set by searching among all possible heuristics the ones that maximize an evaluation function. In this context, we propose a revision approach, dedicated to heuristics represented by production rules, based on the reduction of the search space and on a filtered local search. We present an experiment we carried out in an application domain where heuristics are widely used: cartographic generalization.
Abstraction in Cartographic Generalization Mustière, Sébastien; Saitta, Lorenza; Zucker, Jean-Daniel
Foundations of Intelligent Systems,
2000, 20020702, Letnik:
1932
Book Chapter
This article shows that cartographic generalization is best viewed as representing (formulating, renaming knowledge) and abstracting (simplifying a given representation). The general process of ...creating map is described so as to show how it fits into an abstraction framework developed in artificial intelligence to emphasize the difference between abstraction and representation. The utility of the framework lies in its efficiency to automate knowledge acquisition for the cartographic generalization as a combined acquisition of knowledge for abstraction and knowledge for changing a representation.
Designing a library to support model-oriented generalization Dettori, Giuliana; Puppo, Enrico
Geographic Information Systems: Proceedings of the 6th ACM international symposium on Advances in geographic information systems; 02-07 Nov. 1998,
11/1998
Conference Proceeding
The research aims to give an accurate perception of the role of modern technologies, represented by geographic information systems, in extracting data and information, and then building a ...geographical database for urban land uses in an Ana city to reach generalized models based on the source map with a scale of 1/5000 and then building generalized models through The application of digital generalization elements according to the different standards adopted in the research (1/10000, 1/25000, 1/50000, 1/100000) in order to reach the end to the best results in the cartographic production of these models in a way that facilitates the process of understanding and perception by finding The most appropriate way to represent this data on a map according For the functional importance of each of the uses through disposal to eliminate overlap between uses Depending on the generalization elements of selecting, deleting, simplifying, smoothing, coding and exaggeration, and finally, as The research showed that there is a difference in the areas, uses and preparation of methods when moving from one scale to another. the research concluded by building a three-dimensional model to simulate the natural process of generalizing digital cartography according to different scales.
KEY TECHNOLOGIES FOR 1:10,000 RAPID MAPPING BASED ON GF-7 SATELLITE Liu, J. W.; Liu, J. J.; Zhao, B. L. ...
International archives of the photogrammetry, remote sensing and spatial information sciences.,
01/2023, Letnik:
XLVIII-1/W2-2023
Journal Article, Conference Proceeding
Recenzirano
Odprti dostop
In order to solve the problems of low efficiency, high cost and long cycle of the traditional 1:10000 basic geographic information data production method (aerial photography combined with field ...mapping), this paper proposes a 1:10000 rapid mapping method based on GF-7 satellite data. It solves the key technical difficulties of automatic information recognition, extraction, integration and mapping of water body, building, road, vegetation, contour, and other elements. And an automatic software system of “image -> patch -> data -> map” is developed, which realizes the rapid production of 1:10000 basic geographic information data products and improves the ability of high-precision basic geographic information resource construction.
Map generalization is a process of reducing the contents of a map or data to properly show a geographic feature(s) at a smaller extent. Over the past few years, the fractal way of thinking has ...emerged as a new paradigm for map generalization. A geographic feature can be deemed as a fractal given the perspective of scaling, as its rough, irregular, and unsmooth shape inherently holds a striking scaling hierarchy of far more small elements than large ones. The pattern of far more small things than large ones is a de facto heavy tailed distribution. In this paper, we apply the scaling hierarchy for map generalization to polygonal features. To do this, we firstly revisit the scaling hierarchy of a classic fractal: the Koch Snowflake. We then review previous work that used the Douglas–Peuker algorithm, which identifies characteristic points on a line to derive three types of measures that are long-tailed distributed: the baseline length (d), the perpendicular distance to the baseline (x), and the area formed by x and d (area). More importantly, we extend the usage of the three measures to other most popular cartographical generalization methods; i.e., the bend simplify method, Visvalingam–Whyatt method, and hierarchical decomposition method, each of which decomposes any polygon into a set of bends, triangles, or convex hulls as basic geometric units for simplification. The different levels of details of the polygon can then be derived by recursively selecting the head part of geometric units and omitting the tail part using head/tail breaks, which is a new classification scheme for data with a heavy-tailed distribution. Since there are currently few tools with which to readily conduct the polygon simplification from such a fractal perspective, we have developed PolySimp, a tool that integrates the mentioned four algorithms for polygon simplification based on its underlying scaling hierarchy. The British coastline was selected to demonstrate the tool’s usefulness. The developed tool can be expected to showcase the applicability of fractal way of thinking and contribute to the development of map generalization.
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