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历史地图的自动矢量化:一个基准
Yizi Chen1,2,3, Joseph Chazalon1, Edwin Carlinet1
1EPITA Research Lab. (LRE), Kremlin-Bicêtre, France.
PloS one
|February 15, 2024
概括
这项研究引入了数字化历史地图的改进管道,提高了形状矢量化准确性和完整性,用于详细的城市分析. 这项研究对深度学习方法进行了基准测试,以从历史图谱中可靠地提取特征.
科学领域:
- 计算机视觉 计算机视觉
- 数字人文学科 数字人文学科
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 历史地图的数字化对城市研究至关重要,但现有的形状矢量化方法面临准确性挑战.
- 数字化19世纪和20世纪初的巴黎地图集需要强大的技术来提取复杂的地理细节,如建筑物和街道.
研究的目的:
- 综合评估和改进监督管道,以便对历史地图进行准确和完整的形状矢量化.
- 确定历史地图数字化和特征提取最有效的方法选择.
主要方法:
- 开发了一个监督管道,结合了深边过和分水转换,用于封闭形状提取.
- 提出了改进的训练协议和对边缘检测和形状提取阶段的联合优化.
- 与最先进的深边过器 (包括视觉变压器) 进行了比较,并对传统方法进行了深度可学习分水的评估.
主要成果:
- 建立了历史地图矢量化的基准,使数据,代码和结果公开可用.
- 证明了用于增强矢量化性能的联合优化方法的有效性.
- 确定了关键路径,以完全自动地提取关键的历史地图元素.
结论:
- 拟议的管道和优化方法显著提高了历史地图矢量化的准确性和完整性.
- 这项工作为数字人文和地理信息系统的研究人员提供了宝贵的资源,使新的历史分析成为可能.
- 资源的开放可用性促进了在历史地图学和城市形态学研究中进一步的研究和应用.
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