ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning
Authors: Emma Shelley, Wassim Jabi, Thomas Woolley, Katerina Kaouri
Organizations: School of Computational and Mathematical Sciences, Cardiff University, Cardiff · Welsh School of Architecture, Cardiff University, Cardiff
Floor plan datasets underpin computational analysis of building layouts, spatial connectivity and graph machine learning. However, publicly available datasets often require substantial preprocessing before they can be used reliably. We present ModRes, a cleaned and enriched version of the ResPlan dataset, containing 8,431 valid and unique residential floor plans. Invalid and duplicate floor plans are removed through a dedicated cleaning workflow, producing a ready-to-use dataset. For each floor plan, ModRes provides two-dimensional geometry, images, three-dimensional building representations and three complementary dual graphs in CSV and JSON formats with graph-, node- and edge-level features. Continuous and categorical graph attributes support machine learning workflows. We characterise the dataset using graph-based statistics and demonstrate its utility through a range of case studies. ModRes provides structured geometric, topological and semantic representations needed to automate spatial analysis and support machine learning-based workflows for building design and performance evaluation.
Figures & tables
Figure 1: The planar (primary) and dual graph of a floor plan. Source: [ 14 ] .
Figure 2: An example of a graph in ResPlan. Dark blue lines denote connections through doors, and dark green dashed lines represent connections through an arch. Source: [ 11 ] .
Figure 3: Examples of floor plans stored within different datasets.
Figure 4: Examples of invalid floor plans identified during the dataset filtering process.
Number of bedrooms
Area Threshold ( m2 )
1
[46.5,92.7]
2
[69.7,120.3]
3
[99.4,181.4]
4
[142.6,278.7]
5
[191.9,278.7]
Table 1: Thresholds for removal of floor plans in the size filtering step.
Figure 5: The distribution of floor plan areas in the ResPlan dataset before and after applying the size filtering process.
Cleaning step
Number of plans removed
Percentage of plans removed
No kitchens
84
0.5 %
Invalid doors
627
3.7 %
Missing rooms
1609
9.4 %
Missing walls
372
2.2 %
Disconnected
1606
9.4 %
Door Clusters
2046
12.0 %
Table 2: Number and percentage of floor plans in the ResPlan dataset removed through our cleaning process at each step.
Figure 6: An example of small slivers being removed from the living room of plan 890 using the ‘buffer_shrink_expand’ function.
Figure 7: An example of a door geometry being separated into two distinct doors from plan 15633.
Figure 8: An example of each dual graph type for plan #1158 . From left to right they are the: Full graph, Adjacency Graph and Accessibility graph.
Figure 9: Diagrams showing how different graph features are calculated. Namely, the red areas or length of red line in (c).
Figure 10: An example of high and low compactness floor plans, as calculated using the isoperimetric quotient.
Room type
Measurement
Small
Medium
Large
Extra Large
Living room
Area
(0,20]
(20,40]
(40,60]
(60,∞)
Perimeter
(0,20]
(20,35]
(35,50]
(50,∞)
Bedroom
Area
(0,15]
(15,20]
(20,25]
(25,∞)
Perimeter
(0,15]
(15,20]
(20,25]
(25,∞)
Bathroom
Area
(0,2.5]
(2.5,5]
(5,7.5]
(7.5,∞)
Perimeter
(0,6]
(6,9]
(9,12]
(12,∞)
Table 3: Measurement thresholds for each category for area and perimeter in the categorical dataset. Areas are in m2 and perimeters are in m .
Figure 11: Diagrams showing how different node features are calculated depending on the node type.
Attribute
Entity Type
Data Type
Units
Allowed Range
Encoding Method
Source Method
Available at Inference Time
Bedrooms
Graph
Ordinal
–
0 – 6
Integer encoding
Computed from geometry
Yes
Area
Graph
Continuous
m2
>0
–
Computed from geometry
Yes
Net Area
Graph
Continuous
m2
>0
–
Computed from geometry
Yes
Perimeter
Graph
Continuous
m
>0
–
Computed from geometry
Yes
Compactness
Graph
Continuous
-
0 – 1
–
Isoperimetric quotient
Yes
Unit Type
Graph
Categorical
–
0 – 3
One-hot
Given in ResPlan
Yes
Table 4: Attributes included in the dataset.
Figure 12: Floor plan 67 in two-dimensional and three-dimensional form.
Figure 13: Examples of the geometry images stored for each floor plan.
Figure 14: Frequency of different numbers of bedrooms in ModRes.
Graph
Mean
Variance
Standard Deviation
Skew
Full
20.34
16.90
4.11
0.57
Adjacency
6.60
1.26
1.12
0.80
Accessibility
13.46
6.79
2.61
0.66
Table 5: Variables for the gamma distribution approximation for the number of nodes in each type of graph.
Graph
Mean
Variance
Standard Deviation
Skew
Full
23.06
24.68
4.97
0.63
Adjacency
9.19
4.29
2.07
0.65
Accessibility
12.52
6.89
2.62
0.65
Table 6: Variables for the gamma distribution approximation for the number of edges in each type of graph.
Figure 15: Histograms showing the node and edge distributions for accessibility graphs.
Figure 16: Histograms showing the node and edge distributions for adjacency graphs.
Figure 17: Histograms showing the node and edge distributions for full graphs.
Figure 18: Histograms to show the distribution of density of graphs across all three graph types.
Figure 19: Histograms to show the distribution of the degrees of nodes across all three graph types.
Figure 20: Examples of high and low density graphs from the Modified ResPlan dataset.
Figure 21: Heat maps showing the heating and cooling load per square metre for plan 67 in the Modified ResPlan dataset.
Figure 22: Betweenness centrality for nodes and edges calculated on the accessibility graph for plan 8426.
Figure 23: Visibility analysis on plan 7. The green area is visible from the given vertex, and the red line represents the direction of sight.