Segmentation of Floorplans and Heritage Sites: An Approach to Unbalanced Dataset


  • Sujith Gunjur Umapathy SRH University Berlin, Charlottenburg, Germany
  • Alexander I. Iliev SRH University Berlin, Charlottenburg, Germany; Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria



Deep Learning, Unbalanced Dataset, Semantic Segmentation, Focal Loss, Jaccard Loss


To identify structural deficiencies, floorplan digitization is essential for legacy buildings, and historically significant and culturally rich sites. However, these technologies heavily depend on vector graphics. For an efficient and economical solution, we present an approach with multi-loss functions to handle unbalanced classes in floorplans using U-Net architecture.


BigRentz, Inc. (2022, July 10). How to Understand Floor Plan Symbols . Retrieved from BigRentz:

Carniato, L. (2021, April 6). Multi-Class classification using Focal Loss and LightGBM . Retrieved from Towards Datascience:

Consortium, C. (2015). COCO - Common Objects in Context . Retrieved from COCO - Common Objects in Context: de las Heras, L.-P., Terrades, O., Robles, S., & S’anchez, G. (2015). CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. International Journal on Document Analysis and Recognition .

Dodge, S., Xu, J., & Stenger, B. (2017). Parsing floor plan images. 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA) , 358-361.

Kalervo, A., Ylioinas, J., Häikiö, M., Karhu, A., & Kannala, J. (2019). CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis. arXiv.

Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. arXiv.

Liu, C., Wu, J., Kohli, P., & Furukawa, Y. (2017). Raster-to-Vector: Revisiting Floorplan Transformation. International Conference on Computer Vision (ICCV) (pp. 2214-2222). 2017 IEEE.

Moore, C., & Bell, D. (2020, March 15). Dice similarity coefficient. Reference article . Retrieved from

Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., & Savarese, S. (2019). Generalized Intersection over Union. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) .

Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research , 1929-1958.

van Beers, F., Lindstrm, А., Okafor, Е., & Wiering, М . (2019). Deep Neural Networks with Intersection over Union Loss for Binary Image Segmentation. In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods (pp. 438--445).

Ying, X. (2019). An Overview of Overfitting and its Solutions. Journal of Physics: Conference Series , 022022.

Zaworski, R. (2021, February 14). Medium . Retrieved from Medium:




How to Cite

Gunjur Umapathy, S., & I. Iliev, A. (2022). Segmentation of Floorplans and Heritage Sites: An Approach to Unbalanced Dataset. Digital Presentation and Preservation of Cultural and Scientific Heritage, 12, 205–216.