Abstract Machine learning methods, specifically, convolutional neural networks (CNNs),
have emerged as an integral part of scientific research in many disciplines. However, these …

Identifying the spatial configurations of buildings and grouping them reasonably is an
important task in cartography. This study developed a grouping approach using graph deep …

Building orientation recognition is a fundamental component of cartographic applications
such as map regularization and map generalization. Most current methods effectively …

Building patterns are crucial for urban form understanding, automated map generalization,
and 3 D city model visualization. The existing studies have recognized various building …

建筑群空间分布模式识别对制图综合, 多尺度表达及空间数据挖掘具有重要意义.
针对局部异质性明显的建筑群直线模式识别提取问题, 本文提出基于模板匹配的建筑群组合直线 …

This article presents how a generative adversarial network (GAN) can be employed to
produce a generalised map that combines several cartographic themes in the dense context …

建筑物图形形状相似性能有效用于建筑物匹配, 分类和查询等. 基于DNA
序列比对的Needleman-Wunsch (NW) 算法和Smith-Waterman (SW) 算法 …

The circular cartogram, also known as the Dorling map, is a widely used tool for visualizing
statistical data. It represents regions as circles with their areas in proportion to the statistical …

The shape encoding of geospatial objects is a key problem in the fields of cartography and
geoscience. Although traditional geometric-based methods have made great progress, deep …

建筑物群空间分布模式是重要的城市结构特征. 以往研究多基于格式塔原则将建筑物作为整体认
知分布模式, 忽略了从建筑物局部到整体的视觉认知过程而导致模式的漏识别 …