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Positional accuracy, positional uncertainty, and feature change detection in historical maps: Results of an experiment

机译:历史地图中的位置精度,位置不确定性和特征变化检测:实验结果

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摘要

The measurement and management of positional accuracy and positional uncertainty is especially problematic in historical cartography and Historical GIS applications, for at least two reasons: first, historical sources, and especially historical maps, generally carry a higher degree of positional inaccuracy and uncertainty compared to contemporary geographic databases; second, it is always difficult and often impossible to reliably measure the positional accuracy and positional uncertainty of the spatial attribute of historical data. As an added complication, the terms "inaccuracy" and "uncertainty" are often used as synonyms in the literature, with relatively little attention given to issues of uncertainty. In this article we propose a methodology for detecting the positional inaccuracy and positional uncertainty of measurements of urban change using historical maps at a very high spatial resolution (the building). A widely accepted and routinely employed method for detecting urban change, and spatial change in general, consists in overlaying two or more maps created at different dates, but the technique can lead to the formation of spurious changes-typically, sliver polygons-that are the product of misclassification error or map misalignment rather than actual modifications in land cover. In this paper we develop an algorithm to detect such spurious changes. More in general, we extend the discussion to examine the effects of positional uncertainty and positional inaccuracy in feature change detection analysis. The case-study is the city of Milan, Italy.
机译:位置精度和位置不确定性的测量和管理在历史制图学和历史GIS应用中尤其成问题,至少有两个原因:首先,与当代相比,历史资源(尤其是历史地图)通常具有更高的位置误差和不确定性地理数据库;第二,可靠地测量历史数据的空间属性的位置精度和位置不确定性始终是困难的,而且常常是不可能的。更为复杂的是,术语“不准确性”和“不确定性”在文献中经常用作同义词,对不确定性问题的关注相对较少。在本文中,我们提出了一种方法,该方法使用历史地图以非常高的空间分辨率(建筑物)来检测城市变化测量的位置不准确度和位置不确定性。一种广泛接受且通常采用的检测城市变化和空间变化的方法通常是覆盖在不同日期创建的两个或多个地图,但是该技术可能导致虚假变化(通常是条状多边形)的形成。错误分类错误或地图未对齐的结果,而不是土地覆被的实际修改。在本文中,我们开发了一种算法来检测此类杂散变化。一般而言,我们将讨论扩展到检查特征变化检测分析中位置不确定性和位置不准确性的影响。案例研究是意大利米兰市。

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