ABSTRACT

Historical maps contain a wealth of information not generally available, but they must be referenced to well-known coordinate systems for maximum use in spatial analysis. Existing georeferencing tools are essentially manual, requiring considerable data entry, much panning and zooming, and precise on-screen digitizing. Here we present alternative approaches based on pattern-matching and spatial computing intended to overcome the inefficiencies of standard tools. We also describe and make available two computer programs implementing the methods discussed. The first, designed for large-scale quadrangles, locates map boundaries, finds ground control points, and produces georeferenced images without operator assistance. Experiments show that quadrangle georeferencing can be reliably automated (88% success rate in our tests). A second program, developed for general maps at any scale, uses self-learning and other approaches to overcome most of the manual aspects of georeferencing. Both programs find control points with single-pixel accuracy, yield transform errors on the order of map linewidth, and can produce warped or unwarped images as desired.

摘要

历史地图包含大量通常无法获得的信息，但是必须参考众所周知的坐标系才能在空间分析中最大程度地使用它们。现有的地理配准工具本质上是手动的，需要大量的数据输入，大量的平移和缩放以及精确的屏幕数字化。在这里，我们提出了基于模式匹配和空间计算的替代方法，旨在克服标准工具的低效率问题。我们还将描述并提供实现所讨论方法的两个计算机程序。第一个设计用于大型四边形，可定位地图边界，找到地面控制点，并在无需操作员协助的情况下生成地理参考图像。实验表明，四边形地理配准可以可靠地自动化（在我们的测试中成功率为88％）。第二个程序 专为各种规模的通用地图开发的地图，使用自学习和其他方法来克服地理配准的大多数手动方面。这两个程序都能找到具有单像素精度的控制点，产生映射线宽量级的变换错误，并且可以根据需要生成扭曲或未扭曲的图像。