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

arXiv: https://arxiv.org/abs/2208.08047 · HTML: https://mlsanity.com/paper/2208.08047/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 60 references that are in the map.
- First posted on arXiv 17 Aug 2022.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles](https://mlsanity.com/paper/1603.09246/) (2016)
- [Unsupervised Visual Representation Learning by Context Prediction](https://mlsanity.com/paper/1505.05192/) (2015)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [S4L: Self-Supervised Semi-Supervised Learning](https://mlsanity.com/paper/1905.03670/) (2019)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [Understanding Deep Networks via Extremal Perturbations and Smooth Masks](https://mlsanity.com/paper/1910.08485/) (2019)
- [Billion-scale semi-supervised learning for image classification](https://mlsanity.com/paper/1905.00546/) (2019)
- [Learning Disentangled Representations with Semi-Supervised Deep Generative Models](https://mlsanity.com/paper/1706.00400/) (2017)
- [Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loop](https://mlsanity.com/paper/1512.05227/) (2015)
- [SkyScapes -- Fine-Grained Semantic Understanding of Aerial Scenes](https://mlsanity.com/paper/2007.06102/) (2020)
- [A Deep Learning Approach for Population Estimation from Satellite Imagery](https://mlsanity.com/paper/1708.09086/) (2017)
- [Self-Supervision, Remote Sensing and Abstraction: Representation Learning Across 3 Million Locations](https://mlsanity.com/paper/2203.04445/) (2022)
- [Identifying safe intersection design through unsupervised feature extraction from satellite imagery](https://mlsanity.com/paper/2010.15343/) (2020)
- [Automatic Building Extraction in Aerial Scenes Using Convolutional Networks](https://mlsanity.com/paper/1602.06564/) (2016)

## Built on by (most cited first, 2 of 2)

- [Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition](https://mlsanity.com/paper/2307.15098/) (2023)
- [Scalable Label-efficient Footpath Network Generation Using Remote Sensing Data and Self-supervised Learning](https://mlsanity.com/paper/2309.09446/) (2023)

## 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.