Deep Learning based Semantic Segmentation for Buildings Detection from Remote Sensing Images


Miral J Patel
Hasmukh P Koringa


Building extraction from remote sensing images is the process of automatically identifying and extracting the boundaries of buildings from high-resolution aerial or satellite images. The extracted building footprints can be used for a variety of applications, such as urban planning, disaster management, city development, land management, environmental monitoring, and 3D modeling. The results of building extraction from remote sensing images depend on several factors, such as the quality and resolution of the image and the choice of algorithm.The process of building extraction from remote sensing images typically involves a series of steps, including image pre-processing, feature extraction, and classification. Building extraction from remote sensing images can be challenging due to factors such as varying building sizes and shapes, shadows, and occlusions. However, recent advances in deep learning and computer vision techniques have led to significant improvements in the accuracy and efficiency of building extraction methods. This research presents a deep learning semantic segmentation architecture-based model for developing building detection from high resolution remote sensing images. The open-source Massachusetts dataset is used to train the suggested UNet architecture. The model is optimized using the RMSProp algorithm with a learning rate of 0.0001 for 100 epochs. After 1.52 hours of training on Google Colab the model achieved an 83.55% F1 score, which indicates strong precision and recall.


How to Cite
Patel, M., & koringa, H. (2024). Deep Learning based Semantic Segmentation for Buildings Detection from Remote Sensing Images. International Journal of Next-Generation Computing, 15(1).


  1. Adhikari, S. and Ojha, V. P. 2023. Building detection and counting in convoluted areas using multiclass datasets with unmanned aerial vehicles (uavs) imagery. Advances in Remote Sensing 12, 3, 71–87. DOI:
  2. Alsabhan, W., Alotaiby, T., Dudin, B., et al. 2022. Detecting buildings and nonbuildings from satellite images using u-net. Computational Intelligence and Neuroscience 2022. DOI:
  3. Alshehhi, R., Marpu, P. R., Woon, W. L., and Dalla Mura, M. 2017. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing 130, 139–149. DOI:
  4. Badrinarayanan, V., Kendall, A., and Cipolla, R. 2017. ” segnet: A deep convolutional encoder-decoder architecture for image segmentation,” in ieee transactions on pattern analysis and machine intelligence, vol. 39, no. 12, pp. 2481-2495, dec. 1. DOI:
  5. Femin, A. and Biju, K. 2020. Accurate detection of buildings from satellite images using cnn. In 2020 international conference on electrical, communication, and computer engineering (ICECCE). IEEE, 1–5. DOI:
  6. Guo, H., Liu, Z., Jiang, H., Wang, C., Liu, J., and Liang, D. 2017. Big earth data: A new challenge and opportunity for digital earth’s development. International Journal of Digital Earth 10, 1, 1–12. DOI:
  7. Gupta, A., Ramanath, R., Shi, J., and Keerthi, S. S. 2021. Adam vs. sgd: Closing the generalization gap on image classification. In OPT2021: 13th Annual Workshop on Optimization for Machine Learning.
  8. Hamaguchi, R. and Hikosaka, S. 2018. Building detection from satellite imagery using ensemble of size-specific detectors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 187–191. DOI:
  9. He, K., Zhang, X., Ren, S., and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778. DOI:
  10. Hermosilla, T., Ruiz, L. A., Recio, J. A., and Estornell, J. 2011. Evaluation of automatic building detection approaches combining high resolution images and lidar data. Remote Sensing 3, 6, 1188–1210. DOI:
  11. Jin, X. and Davis, C. H. 2005. Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP Journal on Advances in Signal Processing 2005, 1–11. DOI:
  12. Kala, S. and Jeyakumar, M. 2019. A proficient satellite image building detection using fuzzy and neural network techniques. International Journal of Engineering Research and Technology. 12, 1, 89–96.
  13. Li, J., Huang, X., Tu, L., Zhang, T., and Wang, L. 2022. A review of building detection from very high resolution optical remote sensing images. GIScience & Remote Sensing 59, 1, 1199–1225. DOI:
  14. Long, J., Shelhamer, E., and Darrell, T. 2015. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3431–3440. DOI:
  15. Mnih, V. 2013. Machine learning for aerial image labeling. Ph.D. thesis, University of Toronto.
  16. Mustapha, A., Mohamed, L., and Ali, K. 2021. Comparative study of optimization techniques in deep learning: Application in the ophthalmology field. In Journal of Physics: Conference Series. Vol. 1743. IOP Publishing, 012002. DOI:
  17. Norman, M., Shahar, H. M., Mohamad, Z., Rahim, A., Mohd, F. A., and Shafri, H. Z. M. 2021. Urban building detection using object-based image analysis (obia) and machine learning (ml) algorithms. In IOP Conference Series: Earth and Environmental Science. Vol. 620. IOP Publishing, 012010. DOI:
  18. Parikh, Y. et al. 2022. A systematic analysis of cmr segmentation using deep learning. International Journal of Next-Generation Computing 13, 3. DOI:
  19. Parikh, Y. and Koringa, H. 2023. Left ventricle segmentation using bidirectional convolution dense unet. Journal of Integrated Science and Technology 11, 1, 417–417.
  20. Paris, S., Kornprobst, P., Tumblin, J., Durand, F., et al. 2009. Bilateral filtering: Theory and applications. Foundations and Trends® in Computer Graphics and Vision 4, 1, 1–73. DOI:
  21. Patel, M. J. and Koringa, H. P. 2023. Deep learning architecture u-net based road network detection from remote sensing images. International Journal of Next-Generation Computing 14, 3. DOI:
  22. Patel, M. J. and KotharI, A. 2022. Road network extraction methods from remote sensing images: A review paper. International Journal of Next-Generation Computing 13, 2. DOI:
  23. Patel, M. J. and Kothari, A. M. 2023. Deep learning-enabled road segmentation and edgecenterline extraction from high-resolution remote sensing images. International Journal of Image and Graphics 23, 06, 2350058. DOI:
  24. Patel, M. J., Kothari, A. M., and Koringa, H. P. 2022. A novel approach for semantic segmentation of automatic road network extractions from remote sensing images by modified unet. Radioelectronic and Computer Systems 3, 161–173. DOI:
  25. Prathap, G. and Afanasyev, I. 2018. Deep learning approach for building detection in satellite multispectral imagery. In 2018 International Conference on Intelligent Systems (IS). IEEE, 461–465. DOI:
  26. Ronneberger, O., Fischer, P., and Brox, T. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer, 234–241. DOI:
  27. Simonyan, K. and Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 .
  28. Sirko, W., Kashubin, S., Ritter, M., Annkah, A., Bouchareb, Y. S. E., Dauphin, Y., Keysers, D., Neumann, M., Cisse, M., and Quinn, J. 2021. Continental-scale building detection from high resolution satellite imagery. arXiv preprint arXiv:2107.12283 .
  29. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1–9. DOI:
  30. Wang, H. and Miao, F. 2022. Building extraction from remote sensing images using deep residual u-net. European Journal of Remote Sensing 55, 1, 71–85. DOI:
  31. Yaloveha, V., Podorozhniak, A., and Kuchuk, H. 2022. Convolutional neural network hyperparameter optimization applied to land cover classification. Radioelectronic and computer systems 1, 115–128. DOI:
  32. You, Y., Wang, S., Ma, Y., Chen, G., Wang, B., Shen, M., and Liu, W. 2018. Building detection from vhr remote sensing imagery based on the morphological building index. Remote Sensing 10, 8, 1287. DOI:
  33. Zhang, Y. 1999. Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. ISPRS journal of photogrammetry and remote sensing 54, 1, 50–60. DOI: