Spatial data acquisition

Guidance: Spatial data acquisition is the first task to be carried out in the construction of geographic information system, which can be implemented in a variety of ways including data conversion, remote sensing data processing, digital measurement, etc, the digital input of existing maps is the most widely used means and the most human resource-consuming work. In GIS, the content entered includes spatial information and non-spatial information, the former is the subject of the entry. At present, there are two main ways to input spatial information, namely, hand-held tracking digitization and scanning vectorization, this chapter introduces two methods and related algorithms, such as curve approximation fitting, raster pattern refinement tracking, etc.

After the input of graphics data, various kinds of processing are needed, including coordinate transformation, stitching, etc. In the topology establishment process, various errors need to be modified first, this chapter describes various specific error scenarios, the polygon automatic topology generation algorithm is introduced finally.

  • Summary # Maps are always an important form of information, whether ancient, modern or...
    2023-08-23 01:42:48 UTC
  • Graphic coordinate transformation # After the map is inputted, projection transformati...
    2023-08-23 01:42:48 UTC

Principles, Technologies, and Methods of Geographic Information Systems  102

In recent years, Geographic Information Systems (GIS) have undergone rapid development in both theoretical and practical dimensions. GIS has been widely applied for modeling and decision-making support across various fields such as urban management, regional planning, and environmental remediation, establishing geographic information as a vital component of the information era. The introduction of the “Digital Earth” concept has further accelerated the advancement of GIS, which serves as its technical foundation. Concurrently, scholars have been dedicated to theoretical research in areas like spatial cognition, spatial data uncertainty, and the formalization of spatial relationships. This reflects the dual nature of GIS as both an applied technology and an academic discipline, with the two aspects forming a mutually reinforcing cycle of progress.