Shape cognition in map space using deep auto-encoder learning

被引：0
|
作者：
Yan X. [1 ,2 ]
Ai T. [2 ]
Yang M. [2 ]
Zheng J. [2 ]
机构：
[1] College of Surveying and Geo-Informatics, Tongji University, Shanghai
[2] School of Resource and Environmental Sciences, Wuhan University, Wuhan
来源：
Ai, Tinghua (tinghuaai@whu.edu.cn) | 2021年 / SinoMaps Press卷 / 50期
基金：
中国国家自然科学基金;
关键词：
Auto-encoder; Deep learning; Sequence-to-sequence model; Shape coding; Spatial cognition;
D O I：
10.11947/j.AGCS.2021.20210046
中图分类号：
学科分类号：
摘要：
Shape is an important feature of geospatial objects and a pivotal basis for people to establish spatial concepts and form spatial cognition in map space. The study tries to integrate multiple characteristics of the shape outline using deep auto-encoder learning, and provides support for the mechanism and formalization of spatial cognition. By taking the building data as a case, the study first converts the shape outline into a sequence and extracts its descriptive characteristics by considering the local and regional structures, and then learns a shape coding from the unlabeled data using the sequence-to-sequence learning model. Experiments show that the shape cognition in map space achieves a meaningful similarity measure between different shapes by using deep auto-encoder learning. Furthermore, the shape coding can effectively represent the global and local characteristics in the application scenarios such as shape retrieval and shape matching. © 2021, Surveying and Mapping Press. All right reserved.
引用
收藏
页码：757 / 765
页数：8
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