Advancements in GIS map copyright protection schemes - a critical review

Abstract

Dramatic advancements in Geographic Information Systems (GIS) and computer technologies resulted in a wide availability of rich-knowledge GIS digital vector maps that are easily accessible and downloadable through the world wide web. This led to the need of copyright protection systems to protect the rights of the producers of these maps. Unlike the research on copyright protection for raster/image data types, the research for vector map data is less documented. This article surveys and classifies the GIS vector map copyright protection research papers published between 2000 and 2014, towards a thorough understanding of the-state-of-the-art, addressing significant limitations of previous review articles, and outlining effective recommendations for future research directions.

Research on EPGIS Spatial Information Security for Smart Grid

“…Through digital watermarking technology, copyright control and protection of watermark information embedded in GIS vector data [8], GIS image data and grid space resource data are carried out. At the same time, watermark information is robust and not destroyed by data editing.…”

Evaluating the topological quality of watermarked vector maps

“…Geographical data can be categorized into two types: vector and raster data [4,12]. Vector data represents geographical information by using basic geometrical shapes such as points, lines and polygons [13], while raster data represents information in a matrix of cells or pixels of uniform size (e.g.…”

“…Watermarking of vector map data has been researched for the last 2 decades as a solution for the protection of this type of geographical data [12,[15][16][17][18][19][20][21].…”

“…A key requirement of any watermarking approach is the quality preservation in the watermarked data [12,23]. In the context of vector data, the quality preservation expresses that the original vector map is not affected by the concealed watermark, and is referred to as fidelity.…”

“…In both cases, the emphasis is on the perceptual perspective [24] and is measured with error metrics, such as RMSE (Root Mean Squared Error) and PSNR (Peak Signal to Noise Ratio) (which is based on mean squared error). More details about the metrics used for invisibility of vector data can be found in [12,25,26].…”