Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

Version 1 : Received: 27 January 2021 / Approved: 28 January 2021 / Online: 28 January 2021 (13:06:28 CET)

A peer-reviewed article of this Preprint also exists.

Song, J.; Yu, K. Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs. ISPRS Int. J. Geo-Inf. 2021, 10, 97. Song, J.; Yu, K. Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs. ISPRS Int. J. Geo-Inf. 2021, 10, 97.

Journal reference: ISPRS Int. J. Geo-Inf. 2021, 10, 97
DOI: 10.3390/ijgi10020097

Abstract

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image. (2) region adjacency graph conversion. (3) graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements such as walls, doors, and symbols as well as spatial elements such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

Subject Areas

floor plan analysis; vectorization; graph neural network; indoor spatial data

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