Uhl Johannes H, Leyk Stefan, Chiang Yao-Yi, Duan Weiwei, Knoblock Craig A
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA;
Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USA;
ISPRS Int J Geoinf. 2018 Apr;7(4). doi: 10.3390/ijgi7040148. Epub 2018 Apr 13.
Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographical information contained in such data archives makes it possible to extend geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives (e.g., more than 200,000 map sheets in the United States Geological Survey topographic map archive) and the low graphical quality of older, manually-produced map sheets, the process to extract geographical information from these map archives needs to be automated to the highest degree possible. To understand the potential challenges (e.g., salient map characteristics and data quality variations) in automating large-scale information extraction tasks for map archives, it is useful to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of georeferenced map sheets at different map scales. Such preliminary analytical steps are often neglected or ignored in the map processing literature but represent critical phases that lay the foundation for any subsequent computational processes including recognition. Exemplified for the United States Geological Survey topographic map and the Sanborn fire insurance map archives, we demonstrate how such preliminary analyses can be systematically conducted using traditional analytical and cartographic techniques, as well as visual-analytical data mining tools originating from machine learning and data science.
历史地图是回顾性地理信息的独特来源。最近，几个包含覆盖大空间和时间范围的地图系列的地图档案库已被系统扫描并向公众开放。这些数据档案库中包含的地理信息使得回顾性地扩展地理空间分析成为可能，超越了数字制图时代。然而，鉴于此类档案库的数据量巨大（例如，美国地质调查局地形图档案库中有超过20万张地图）以及旧的手工制作地图的图形质量较低，从这些地图档案库中提取地理信息的过程需要尽可能高度自动化。为了了解在为地图档案库自动化大规模信息提取任务时可能面临的挑战（例如，显著的地图特征和数据质量变化），高效评估不同地图比例尺下地理参考地图的时空覆盖范围、大致地图内容和空间精度是很有用的。在地图处理文献中，这些初步分析步骤常常被忽视或忽略，但它们是为包括识别在内的任何后续计算过程奠定基础的关键阶段。以美国地质调查局地形图和桑伯恩火灾保险地图档案库为例，我们展示了如何使用传统分析和制图技术以及源自机器学习和数据科学的视觉分析数据挖掘工具系统地进行此类初步分析。
