Urban Feature Analysis from Aerial Remote Sensing Imagery Using Self-Supervised and Semi-Supervised Computer Vision

This study applies self-supervised and semi-supervised computer vision techniques to analyse urban features from aerial remote sensing imagery. The approach enables scalable urban environment characterisation, leveraging modern representation learning methods to extract meaningful features from large-scale aerial imagery datasets.

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Recommended citation:

S. Seneviratne, J. S. Wijnands, K. Nice, H. Zhao, B. Godic, S. Mavoa, R. Vidanaarachchi, M. Stevenson, L. Garcia, R. F. Hunter et al., “Urban Feature Analysis from Aerial Remote Sensing Imagery Using Self-Supervised and Semi-Supervised Computer Vision,” arXiv preprint arXiv:2208.08047, 2022.

BibTeX

@article{seneviratne2022urban,
  title={Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision},
  author={Seneviratne, Sachith and Wijnands, Jasper S and Nice, Kerry and Zhao, Haifeng and Godic, Branislava and Mavoa, Suzanne and Vidanaarachchi, Rajith and Stevenson, Mark and Garcia, Leandro and Hunter, Ruth F and others},
  journal={arXiv preprint arXiv:2208.08047},
  year={2022}
}