Shape cognition in map space using deep auto-encoder learning

doi:10.11947/j.AGCS.2021.20210046

Abstract

Abstract: 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.

Key words: spatial cognition, shape coding, deep learning, auto-encoder, sequence-to-sequence model

CLC Number:

P208

YAN Xiongfeng, AI Tinghua, YANG Min, ZHENG Jianbin. Shape cognition in map space using deep auto-encoder learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(6): 757-765.

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