Masonry Wall Image Analysis Benchmark

We have proposed in [1] a novel machine learning based fully automatic approach on the semantic analysis and documentation of masonry wall images, performing in parallel automatic detection and virtual completion of occluded or damaged wall regions, and brick segmentation leading to an accurate model of the wall structure. For training and testing the network a new dataset has been created, and an extensive qualitative and quantitative evaluation versus the state-of-the-art is given. The experiments confirmed that the proposed method outperforms the reference techniques both in terms of wall structure estimation and regarding the visual quality of the inpainting step, moreover it can be robustly used for various different masonry wall types.

The masonry wall image set and the manually annotated mortar-brick-occlusion masks used for training and testing the proposed method can be downloaded below.

Benchmark images

Terms of usage:
The benchmark set is free for scientific use.

  • Please acknowledge the use of the benchmark by referring to our related publication [1].
  • Please notify us if a publication using the benchmark set appears.

Download the benchmark image set from here (235 MB) .

If you have questions, please contact Csaba Benedek in e-mail:


[1] Y. Ibrahim, B. Nagy and Cs. Benedek: ”Deep Learning-based Masonry Wall Image analysis,” Remote Sensing, vol. 12, no. 23, article 3918, 2020, IF: 4.848 Open Access


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GeoComp Group leader: Dr. Csaba Benedek

i4D project manager: Dr. Zsolt Jankó

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