# ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning

arXiv: https://arxiv.org/abs/2610.17178 · HTML: https://mlsanity.com/paper/2610.17178/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 5 of 5)

- [CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis](https://mlsanity.com/paper/1904.01920/) (2019)
- [Robot Localization in Floor Plans Using a Room Layout Edge Extraction Network](https://mlsanity.com/paper/1903.01804/) (2019)
- [MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes](https://mlsanity.com/paper/2407.10121/) (2024)
- [ResPlan: A Large-Scale Vector-Graph Dataset of 17,000 Residential Floor Plans](https://mlsanity.com/paper/2508.14006/) (2025)
- [Room Classification on Floor Plan Graphs using Graph Neural Networks](https://mlsanity.com/paper/2108.05947/) (2021)

## Abstract (arXiv)

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.