mlsanityData to 7 Oct 2026

Urban feature analysis from aerial remote sensing imagery using self-supervised and semi-supervised computer vision

arXiv 2208.08047 ↗First posted 17 Aug 2022cs.CVcited by 2

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Abstract (arXiv)

Analysis of overhead imagery using computer vision is a problem that has received considerable attention in academic literature. Most techniques that operate in this space are both highly specialised and require expensive manual annotation of large datasets. These problems are addressed here through the development of a more generic framework, incorporating advances in representation learning which allows for more flexibility in analysing new categories of imagery with limited labeled data. First, a robust representation of an unlabeled aerial imagery dataset was created based on the momentum contrast mechanism. This was subsequently specialised for different tasks by building accurate classifiers with as few as 200 labeled images. The successful low-level detection of urban infrastructure evolution over a 10-year period from 60 million unlabeled images, exemplifies the substantial potential of our approach to advance quantitative urban research.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 60 references that are in the map. First posted on arXiv 17 Aug 2022.

Builds on

24 of 60 references.

References · 24

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12 more
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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognitioncited by 2
  2. 2023Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learningcited by 0