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

Download SFS-A68 dataset

1. Introduction:

Analyzing building models for quantity takeoff, 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. This study addresses a gap in existing space function classification work, which is a lack of datasets to train and evaluate machine learning and deep learning models. We present the SFS-A68 dataset for space function segmentation in apartment buildings. The dataset consists of input and ground truth images for 68 floor plans of apartment buildings designed or built between 1952 and 2019.

Input Image	Ground Truth

Each pixel in a ground truth image is assigned to a space function class. Space function classes in apartment buildings that are classified by the space function segmentation network are shown in Table below, right column. We have identified 22 space function classes for the apartment buildings in our dataset. Each layout element in an input image is colored according to a unique class color (Table below, left column). Space element classes may be helpful as metadata to determine space function classes. The defined class hierarchy of the dataset with the unique RGB color code of each class can be seen below.

Layout element classes

Space function classes

[root] ├── [Space] │ ├── (102,102,122)[InternalSpace] │ └── (161,162,155)[ExternalSpace] └── [SpaceElement] ├── [SpaceContainedElement] │ ├── [CirculationElement] │ │ ├── (230, 184, 175)[FlightOfStairs] │ │ └── (102, 1, 30)[Landing] │ ├── [FurnishingElement] │ │ ├── (253, 223, 162)[KitchenElement] │ │ └── (248, 193, 79)[SanitaryElemen] │ └── [EquipmentElement] │ └── [HomeAppliance] │ └── (159, 140, 81)[TextileCareAppliance] └── [SpaceEnclosingElement] ├── (109, 189, 110)[Opening] ├── (255, 107, 0)[Partition] ├── (200, 255, 0)[Window] └── [Door] ├── (0, 255, 0)[RegularDoor] ├── (72, 112, 39)[UnitDoor] └── (187, 244, 154)[ElevatorDoor]

[Space] ├── [ResidentialSpace] │ ├── [CommunalSpace] │ │ ├── (107,74,101)[DiningRoom] │ │ ├── (166, 206, 227)[FamilyRoom] │ │ └── (242, 0, 192)[LivingRoom] │ └── [PrivateSpace] │ ├── (0, 255, 248)[Bedroom] │ │ ├── (0,175,175)[MasterBedroom] │ │ └── (4,72,148)[BoxRoom] │ └── (194, 123, 160)[HomeOffice] ├── [ServiceSpace] │ ├── (253, 237, 0)[Shaft] │ ├── (255, 182, 0)[StorageRoom] │ │ └── (191, 144, 0)[WalkInCloset] │ └── [SanitarySpace] │ ├── (255, 0, 0)[Bathroom] │ ├── (69, 129, 142)[Toilet] │ ├── (131, 126, 197)[Kitchen] │ └── (0, 0, 255)[LaundryRoom] ├── [CirculationSpace] │ ├── [VerticalCirculationSpace] │ │ ├── (9, 244, 156)[Elevator] │ │ └── (168, 134, 113)[Stairway] │ └── [HorizontalCirculationSpace] │ ├── (151, 143, 141)[Entrance] │ └── (125, 62, 32)[Hallway] │ ├── (225, 138, 96)[MainHallway] │ └── (155, 94, 65)[InternalHallway] └── [ExternalSpace] ├── (255, 255, 22)[AccessBalcony] └── (120, 56, 145)[Loggia]

2. Prerequisites

In order to use the helper codes, create the environment from the environment.yml file

    conda env create -f environment.yml

Activate the new environment:

    conda activate SFS-A68

Verify that the new environment was installed correctly

    conda env list

3. Dataset and Processing:

We first used the dataset to develop two segmentation models based on transfer learning and training from scratch then, the outputs of the space function segmentation models for the test dataset are evaluated by Intersection over Union (IoU) and Total Error metrics.

4. Acknowledgments

The authors (Amir Ziaee, Georg Suter) gratefully acknowledge support by Grant Austrian Science Fund (FWF): I 5171-N, Mihael Barada, and participants in course '259.428-2021S Architectural Morphology' at TU Wien for data collection.