Machines Reading Maps: Finding and Understanding Text on Maps

“机器阅读地图”(MRM)旨在改变人文主义者和遗产专业人士与地图图像互动的方式。地图是全球文化遗产的重要组成部分，在美国和英国，地图正在以快速的速度被扫描。然而，对地图的最关键的调查仍在小范围内进行，通过仔细阅读几张地图。单独的地图通过视觉语法进行交流，并辅之以文本。但地图上的文字几乎完全是一个未被开发的来源，可以用来理解地点知识是如何构建的。对地图内容进行比例尺调查可以让我们了解地图记录中保留和遗漏了什么。这些知识是理解为什么使用地图文本来丰富收集元数据可能是可取的(当收集记录缺乏任何或仅仅是最肤浅的地理或位置信息时)或可能有害的(当地图文本复制殖民权力结构时)的关键起点。此外，正确的地图可能很难找到。大型地图集合往往属于那些很少在项目级别进行编目的集合：一条记录将捕捉数十张(如果不是数千张)图纸的元数据。这是一个众所周知的研究障碍，它制作了历史地图，比如报纸等连载来源，挑战了人文学科的来源。我们展望了未来，地图集合可以根据其空间内容进行搜索，就像数字化的报纸集合支持跨扫描页面的全文搜索一样。这一项目有助于扭转历史地图收藏的命运，因为目前许多历史地图收藏都可以在网上获得。MRM将使研究人员和文化机构能够跨收藏和机构生成和分析这些数据，促进元数据的创建和非殖民化工作，并提高未编目或最低限度编目的纸张的可获取性和可发现性。MRM建立在项目团队在历史地图和地图处理方面的专业知识之上。重要的是，它改进了一个已经很强大的从地图中提取文本的工具(Strabo)，该工具由美国Co-I和链接地图项目的同事开发。先进的处理新型地图的软件工具对于使文本提取成为一种可以在世界各地的图书馆和档案馆使用的方法至关重要。MRM从扫描的地图集合中生成数据，并在地图和数据馆长、元数据和数字学术专家、历史学家以及地理信息和数据科学家之间建立社区。这项工作与苏格兰国家图书馆(NLS)、大英图书馆(BL)和国会图书馆(LC)的合作伙伴合作，他们拥有广泛的扫描地图收藏，这项工作统一了关于19世纪工业化空间经验的研究问题。英国和美国城市在20世纪的社会变化与GIScience在使用计算方法处理比例历史地图方面的专业知识。通过预测地图上的文本所代表的内容类型(道路、建筑、山脉等)链接到地名词典(地点和相关元数据的索引，如位置)，我们释放了用户查找和解释数以千计的地图的潜力。文化机构可以将地图文本数据反馈到其工作中，以研究其收藏的地理覆盖范围，或调查现有元数据与地图标签报告位置之间的差异。例如，在逐页的基础上，主题和主题的MARC字段可以通过地图文本来丰富。在处理了美国和英国的地图并将它们链接到历史地名录之后，我们测试了将英国地图标签与苏格兰贸易目录相关联，并将桑伯恩地图数据与美国人口普查记录进行匹配-这对英国和美国的数字历史数据都做出了重大贡献。这些研究测试案例例证了地图标签作为主要来源的多功能性。

'Machines Reading Maps' (MRM) aims to change the way that humanists and heritage professionals interact with map images. Maps constitute a significant body of global cultural heritage, and they are being scanned at a rapid pace in the US and UK. However, most critical investigation of maps continues on a small scale, through close 'readings' of a few maps. Individual maps communicate through visual grammars, supplemented by text. But text on maps is an almost entirely untapped source for understanding how knowledge of place is constructed. Investigating map content at scale can teach us about what has been preserved and omitted in the cartographic record. Such knowledge is a key starting point for understanding why using map text to enrich collection metadata may be advisable (when collection records lack any or only the most superficial geographic or locational information) or potentially harmful (when map text replicates colonial power structures). Additionally, the right maps can be hard to find. Large map collections tend to be among those that are rarely catalogued at item level: one record is meant to capture the metadata for dozens, if not thousands, of sheets. This is a well-known research obstacle, one that has made historical maps, like serialized sources such as newspapers, challenging sources in the humanities. We envision a future where map collections can be searched based on their spatial content, similar to the way that digitised newspaper collections enable full-text searching across scanned pages. This project contributes to reversing the fortunes of historic map collections at the moment when many of them are being made available online. MRM will enable researchers and cultural institutions to generate and analyze this data across collections and institutions, contributing to metadata creation and decolonization efforts, and enhancing accessibility and discoverability of un- or minimally-catalogued sheets. MRM builds on the project team's expertise around historical maps and map processing. Importantly, it refines an already robust tool for extracting text from maps (Strabo), developed by the US Co-I and colleagues on the Linked Maps project. Advancing software tools for handling new types of maps is essential to making text extraction a method that can be used in libraries and archives around the world. MRM generates data from scanned map collections and builds community among map and data curators, metadata and digital scholarship specialists, historians, and geographic information and data scientists. Working with partners at the National Library of Scotland (NLS), British Library (BL) and the Library of Congress (LC) who have extensive scanned map collections, this work unites research questions about the spatial experience of industrialization in 19th-c. Great Britain and social change in US cities during the 20th c. with GIScience expertise in using computational methods to process historical maps at scale. By predicting what type of content text on maps represents (roads, buildings, mountains, etc.) and linking to gazetteers (indexes of places and related metadata, like locations), we unlock the potential for users to find and interpret maps by the thousands. Cultural institutions can feed map text data back into their work to study the geographical coverage of their collections, or investigate differences between existing metadata and reported locations of map labels. On a sheet-by-sheet basis, for example, MARC fields for subjects and topics can be enriched by map text. After processing US and UK maps and linking them to historical gazetteers, we test linking UK map labels to Scottish Trade Directories, and matching Sanborn map data to US census records-making a significant contribution to both British and American digital historical data. Such research test cases exemplify the versatility of map labels as primary sources.