Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale

<em>Modified Swiss Dwellings</em>The Modified Swiss Dwellings (MSD) dataset is a <strong>machine learning-ready dataset for floor plan auto-completion at scale</strong>. The MSD dataset is derived from the Swiss Dwellings database (v3.0.0). The MSD dataset (train split) contains <strong>4167 floor plans</strong> of <strong>single- as well as multi-unit building complexes across Switzerland</strong>, hence extending the building scale w.r.t. of other well know floor plan datasets like the RPLAN dataset. Since the MSD dataset will be part of a challenge @ ICCV in Paris, 2023, October 3, the <strong>test split is not yet made public</strong>. This will be added after the submission deadline of the challenge, which will be around mid September 2023.<br><em>Cleaning, filtering, and processing</em>All cleaning, filtering, and processing is done in Python. The Swiss Dwellings database is cleaned and filtered on residential building complexes that have a minimum room count (&gt;10) and have at least 2 "Zone 2" rooms (<em>e.g.</em>, living room, corridor, kitchen, dining). A graph extraction algorithm fully based on the `shapely` and `networkx` libraries in Python was developed to extract the access graphs from the filtered floor plans.<br><em>Dataset structure</em>The MSD dataset contains 4 folders: `graph_in` [&lt;index&gt;.pickle], `struct_in` [&lt;index&gt;.npy], `full_out` [&lt;index&gt;.npy], and `graph_out` [&lt;index&gt;.pickle]. Naming is consistent across all folders, meaning that an instance from `graph_in` with name "&lt;index&gt;.pickle" is from the same floor plan as an instance from `full_out` with name "&lt;index&gt;.npy".<br><em>Floor plan auto-completion</em>The MSD dataset is developed with the goal for the computer science community to develop (deep learning) models for the task of floor plan auto-completion. The floor plan auto-completion task takes as input the boundary of a building, the structural elements necessary for the building’s structural integrity, and a set of user constraints formalized in a graph structure, with the goal of automatically generating the full floor plan. Specifically, the goal is to learn the correlation between the the joint distribution of `graph_in` and `struct_in` with that of `full_out`. `graph_out` is provided when researchers want to use / develop methods from graph signal processing, or graph machine learning specifically.<br><em>GIthub guidelines</em>Github page.