论文标题
SFS-A68：用于分割公寓楼空间功能的数据集
SFS-A68: a dataset for the segmentation of space functions in apartment buildings
论文作者
论文摘要
分析可用区域，建筑安全或能量分析的建筑模型需要空间和相关对象的功能分类数据。需要自动空间功能分类以减少输入模型准备工作和错误。现有的空间功能分类器使用空间特征向量或空间连接图作为输入。尚未研究深度学习（DL）图像分割方法在空间功能分类中的应用。作为解决这一差距的第一步，我们提出了一个数据集SFS-A68，该数据集由68个数字3D公寓楼太空布局产生的输入和地面真相图像组成。该数据集适用于开发用于空间函数分割的DL模型。我们使用数据集来训练和评估基于转移学习和从头开始培训的实验空间函数分割网络。测试结果证实了DL图像分割在空间函数分类中的适用性。实验的代码和数据集可在线公开获得（https://github.com/a2amir/sfs-a68）。
Analyzing building models for usable area, building safety, or energy analysis requires function classification data of spaces and related objects. Automated space function classification is desirable to reduce input model preparation effort and errors. Existing space function classifiers use space feature vectors or space connectivity graphs as input. The application of deep learning (DL) image segmentation methods to space function classification has not been studied. As an initial step towards addressing this gap, we present a dataset, SFS-A68, that consists of input and ground truth images generated from 68 digital 3D models of space layouts of apartment buildings. The dataset is suitable for developing DL models for space function segmentation. We use the dataset to train and evaluate an experimental space function segmentation network based on transfer learning and training from scratch. Test results confirm the applicability of DL image segmentation for space function classification. The code and the dataset of the experiments are publicly available online (https://github.com/A2Amir/SFS-A68).
