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Optimized building extraction from high-resolution satellite imagery using deep learning

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
  • Published:
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Abstract

Building extraction is very essential in various urban dynamics like disaster management and change detection, finding the estimated population, and so on. Building extraction from satellite data is a challenging task as the images may be subjected to different illumination or structure due to very large variations of the appearance of buildings which may correspond to the different area/terrain. Although satellite imagery is readily available from various sources, translating the imagery includes intensive effort. Many computer-vision tasks have been carried out successfully but understanding the impact of them on building extraction with remote sensing imagery is a growing need.To overcome this kind of problem, an algorithm is proposed which extends the convolutional neural network for pixel-wise classification of images. Furthermore, to resolve the problem of extraction and masking of images, Mask-RCNN (i.e., Mask Region-based Convolutional Neural Network) algorithm is used which makes this process easier and more efficient.The model is trained on a complex dataset that is significantly larger. Also, to make this algorithm more scalable, an advanced image augmentation technique is used in the pre-processing step.The results show that the algorithm achieves better performance in terms of accuracy.

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Data availability

The data samples have been taken using crowdAI.

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The relevant code with the manuscript is also avail- able and would be available, if will be asked to do so later.

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Acknowledgements

The authors would like to express gratitude to Department of Technical Education and Panipat Institute of Engineering & Technology, Panipat, India. The authors would also like to thank to Vice Chancellor, Dr. A.P.J. Abdul Kalam Technical University, and Uttar Pradesh, India.

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Authors and Affiliations

  1. Research Engineer, Ubisoft, Mumbai, India

    Ramesh Raghavan

  2. Department of Computer Applications, Panipat Institute of Engineering & Technology, Panipat, Haryana, India

    Dinesh Chander Verma

  3. Department of Technical Education, IET, Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, 226021, India

    Digvijay Pandey

  4. Department of ECE, G.B.Pant DSEU Okhla-1 Campus (Formerly G.B.Pant Engineering College), New Delhi, India

    Rohit Anand

  5. Department of Information Technology, College of Technology, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, Udham Singh Nagar, Uttrakhand, India

    Binay Kumar Pandey

  6. Department of CS & IT, Sant Baba Attar Singh Khalsa College, Sandaur, Punjab, India

    Harinder Singh

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  1. Ramesh Raghavan
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  2. Dinesh Chander Verma
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  3. Digvijay Pandey
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  5. Binay Kumar Pandey
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Correspondence to Digvijay Pandey.

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Raghavan, R., Verma, D.C., Pandey, D. et al. Optimized building extraction from high-resolution satellite imagery using deep learning. Multimed Tools Appl 81, 42309–42323 (2022). https://doi.org/10.1007/s11042-022-13493-9

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