{"data":{"id":"10.4121/e1d89cb5-6872-48fc-be63-aadd687ee6f9","type":"dois","attributes":{"doi":"10.4121/e1d89cb5-6872-48fc-be63-aadd687ee6f9","prefix":"10.4121","suffix":"e1d89cb5-6872-48fc-be63-aadd687ee6f9","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"van Engelenburg, Casper","givenName":"Casper","familyName":"van Engelenburg","affiliation":[],"nameIdentifiers":[]},{"name":"Khademi, Seyran","givenName":"Seyran","familyName":"Khademi","affiliation":[],"nameIdentifiers":[]},{"name":"Mostafavi, Fatemeh","givenName":"Fatemeh","familyName":"Mostafavi","affiliation":[],"nameIdentifiers":[]},{"name":"Standfest, Matthias","givenName":"Matthias","familyName":"Standfest","affiliation":[],"nameIdentifiers":[]},{"name":"Franzen, Michael ","givenName":"Michael ","familyName":"Franzen","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale"}],"publisher":"4TU.ResearchData","container":{},"publicationYear":2023,"subjects":[{"subject":"Artificial Intelligence and Image Processing","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"0801"},{"subject":"FOS: Computer and information sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Building","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1202"},{"subject":"FOS: Civil engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Architecture","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"1201"},{"subject":"Built Environment and Design","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"12"},{"subject":"Information and Computing Sciences","subjectScheme":"Australian and New Zealand Standard Research Classification (ANZSRC), 2008","classificationCode":"08"},{"subject":"floor plan generation"},{"subject":"floor plan auto-completion"},{"subject":"computer vision"},{"subject":"graph machine learning"},{"subject":"access graph"},{"subject":"functional graph"}],"contributors":[{"name":"TU Delft, Faculty Of Architecture And Built Environment, Department Of Architecture","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]},{"name":"Archylise A.G","nameType":"Organizational","affiliation":[],"contributorType":"Other","nameIdentifiers":[]}],"dates":[{"date":"2023-07-11","dateType":"Issued"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":["zip of folders; containing .pickle and .npy files"],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"\u003cem\u003eModified Swiss Dwellings\u003c/em\u003eThe Modified Swiss Dwellings (MSD) dataset is a \u003cstrong\u003emachine learning-ready dataset for floor plan auto-completion at scale\u003c/strong\u003e. The MSD dataset is derived from the Swiss Dwellings database (v3.0.0). The MSD dataset (train split) contains \u003cstrong\u003e4167 floor plans\u003c/strong\u003e of \u003cstrong\u003esingle- as well as multi-unit building complexes across Switzerland\u003c/strong\u003e, 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 \u003cstrong\u003etest split is not yet made public\u003c/strong\u003e. This will be added after the submission deadline of the challenge, which will be around mid September 2023.\u003cbr\u003e\u003cem\u003eCleaning, filtering, and processing\u003c/em\u003eAll 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 (\u0026gt;10) and have at least 2 \"Zone 2\" rooms (\u003cem\u003ee.g.\u003c/em\u003e, 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.\u003cbr\u003e\u003cem\u003eDataset structure\u003c/em\u003eThe MSD dataset contains 3 files.\u003cbr\u003e1) A README.md file explaining the dataset.2) A training set ZIP archive, containing 4 folders: `graph_in` [\u0026lt;index\u0026gt;.pickle], `struct_in` [\u0026lt;index\u0026gt;.npy], `full_out` [\u0026lt;index\u0026gt;.npy], and `graph_out` [\u0026lt;index\u0026gt;.pickle]. Naming is consistent across all folders, meaning that an instance from `graph_in` with name \"\u0026lt;index\u0026gt;.pickle\" is from the same floor plan as an instance from `full_out` with name \"\u0026lt;index\u0026gt;.npy\".3) A test set ZIP archive, containing 2 folders: `graph_in` and `struct_in` (similarly structured as the training data; but obviously with withheld annotations.)\u003cbr\u003e\u003cem\u003eFloor plan auto-completion\u003c/em\u003eThe 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.\u003cbr\u003e\u003cem\u003eGIthub guidelines\u003c/em\u003eGithub page.","descriptionType":"Abstract"}],"geoLocations":[{"geoLocationPlace":"Switzerland"}],"fundingReferences":[],"xml":"<?xml version="1.0" encoding="UTF-8"?>
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  <identifier identifierType="DOI">10.4121/E1D89CB5-6872-48FC-BE63-AADD687EE6F9</identifier>
  <creators>
    <creator>
      <creatorName>Casper van Engelenburg</creatorName>
      <givenName>Casper</givenName>
      <familyName>van Engelenburg</familyName>
    </creator>
    <creator>
      <creatorName>Seyran Khademi</creatorName>
      <givenName>Seyran</givenName>
      <familyName>Khademi</familyName>
    </creator>
    <creator>
      <creatorName>Fatemeh Mostafavi</creatorName>
      <givenName>Fatemeh</givenName>
      <familyName>Mostafavi</familyName>
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    <creator>
      <creatorName>Matthias Standfest</creatorName>
      <givenName>Matthias</givenName>
      <familyName>Standfest</familyName>
    </creator>
    <creator>
      <creatorName>Michael  Franzen</creatorName>
      <givenName>Michael </givenName>
      <familyName>Franzen</familyName>
    </creator>
  </creators>
  <titles>
    <title>Modified Swiss Dwellings: a Machine Learning-ready Dataset for Floor Plan Auto-Completion at Scale </title>
  </titles>
  <publisher>4TU.ResearchData</publisher>
  <publicationYear>2023</publicationYear>
  <resourceType resourceTypeGeneral="Dataset">Dataset</resourceType>
  <subjects>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="0801">Artificial Intelligence and Image Processing</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="1202">Building</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="1201">Architecture</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="12">Built Environment and Design</subject>
    <subject subjectScheme="Australian and New Zealand Standard Research Classification (ANZSRC), 2008" classificationCode="08">Information and Computing Sciences</subject>
    <subject>floor plan generation</subject>
    <subject>floor plan auto-completion</subject>
    <subject>computer vision</subject>
    <subject>graph machine learning</subject>
    <subject>access graph</subject>
    <subject>functional graph</subject>
  </subjects>
  <contributors>
    <contributor contributorType="Other">
      <contributorName nameType="Organizational">TU Delft, Faculty of Architecture and Built Environment, Department of Architecture</contributorName>
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    <contributor contributorType="Other">
      <contributorName nameType="Organizational">Archylise A.G</contributorName>
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  <dates>
    <date dateType="Issued">2023-07-11</date>
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  <language>en</language>
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  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;em&gt;Modified Swiss Dwellings&lt;/em&gt;&lt;/p&gt;&lt;p&gt;The Modified Swiss Dwellings (MSD) dataset is a &lt;strong&gt;machine learning-ready dataset for floor plan auto-completion at scale&lt;/strong&gt;. The MSD dataset is derived from the &lt;a href="https://zenodo.org/record/7788422" target="_blank"&gt;Swiss Dwellings database&lt;/a&gt; (v3.0.0). The MSD dataset (train split) contains &lt;strong&gt;4167 floor plans&lt;/strong&gt; of &lt;strong&gt;single- as well as multi-unit building complexes across Switzerland&lt;/strong&gt;, hence extending the building scale w.r.t. of other well know floor plan datasets like the &lt;a href="http://staff.ustc.edu.cn/~fuxm/projects/DeepLayout/index.html" target="_blank"&gt;RPLAN&lt;/a&gt; dataset. Since the MSD dataset will be part of a challenge @ ICCV in Paris, 2023, October 3, the &lt;strong&gt;test split is not yet made public&lt;/strong&gt;. This will be added after the submission deadline of the challenge, which will be around mid September 2023.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Cleaning, filtering, and processing&lt;/em&gt;&lt;/p&gt;&lt;p&gt;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 (&amp;gt;10) and have at least 2 "Zone 2" rooms (&lt;em&gt;e.g.&lt;/em&gt;, 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.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Dataset structure&lt;/em&gt;&lt;/p&gt;&lt;p&gt;The MSD dataset contains 3 files.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;1) A README.md file explaining the dataset.&lt;/p&gt;&lt;p&gt;2) A training set ZIP archive, containing 4 folders: `graph_in` [&amp;lt;index&amp;gt;.pickle], `struct_in` &lt;span style="background-color: rgb(255, 250, 234);"&gt;[&amp;lt;index&amp;gt;.npy]&lt;/span&gt;, `full_out` &lt;span style="background-color: rgb(255, 250, 234);"&gt;[&amp;lt;index&amp;gt;.npy]&lt;/span&gt;, and `graph_out` &lt;span style="background-color: rgb(255, 250, 234);"&gt;[&amp;lt;index&amp;gt;.pickle]&lt;/span&gt;. Naming is consistent across all folders, meaning that an instance from `graph_in` with name "&amp;lt;index&amp;gt;.pickle" is from the same floor plan as an instance from `full_out` with name "&amp;lt;index&amp;gt;.npy".&lt;/p&gt;&lt;p&gt;&lt;span style="background-color: rgb(255, 250, 234);"&gt;3) A test set ZIP archive, containing 2 folders: `graph_in` and `struct_in` (similarly structured as the training data; but obviously with withheld annotations.)&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Floor plan auto-completion&lt;/em&gt;&lt;/p&gt;&lt;p&gt;The &lt;span style="background-color: rgb(255, 250, 234);"&gt;MSD &lt;/span&gt;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.&amp;nbsp;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.&lt;/p&gt;&lt;p&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;GIthub guidelines&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;a href="https://github.com/cvaad-workshop/iccv23-challenge.git" target="_blank"&gt;Github page&lt;/a&gt;.&lt;/p&gt;</description>
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    <geoLocation>
      <geoLocationPlace>Switzerland</geoLocationPlace>
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