# SFS-A68: a dataset for the segmentation of space functions in apartment buildings

arXiv: https://arxiv.org/abs/2209.09094 · HTML: https://mlsanity.com/paper/2209.09094/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Empirical Evaluation of Rectified Activations in Convolutional Network](https://mlsanity.com/paper/1505.00853/) (2015)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [U-Net and its variants for medical image segmentation: theory and applications](https://mlsanity.com/paper/2011.01118/) (2020)
- [Normalization Techniques in Training DNNs: Methodology, Analysis and Application](https://mlsanity.com/paper/2009.12836/) (2020)
- [IFCNet: A Benchmark Dataset for IFC Entity Classification](https://mlsanity.com/paper/2106.09712/) (2021)
- [Visual Analytics and Human Involvement in Machine Learning](https://mlsanity.com/paper/2005.06057/) (2020)

## Built on by (most cited first, 1 of 1)

- [SAGC-A68: a space access graph dataset for the classification of spaces and space elements in apartment buildings](https://mlsanity.com/paper/2307.04515/) (2023)

## Abstract (arXiv)

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).