# Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution

arXiv: https://arxiv.org/abs/2204.01736 · HTML: https://mlsanity.com/paper/2204.01736/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 16 of 16)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Functional Map of the World](https://mlsanity.com/paper/1711.07846/) (2017)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [xBD: A Dataset for Assessing Building Damage from Satellite Imagery](https://mlsanity.com/paper/1911.09296/) (2019)
- [Using satellite imagery to understand and promote sustainable development](https://mlsanity.com/paper/2010.06988/) (2020)
- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)
- [The Effects of Super-Resolution on Object Detection Performance in Satellite Imagery](https://mlsanity.com/paper/1812.04098/) (2018)
- [Generating Interpretable Poverty Maps using Object Detection in Satellite Images](https://mlsanity.com/paper/2002.01612/) (2020)
- [Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis](https://mlsanity.com/paper/2106.11485/) (2021)
- [The SpaceNet Multi-Temporal Urban Development Challenge](https://mlsanity.com/paper/2102.11958/) (2021)
- [Predicting Landsat Reflectance with Deep Generative Fusion](https://mlsanity.com/paper/2011.04762/) (2020)
- [Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images](https://mlsanity.com/paper/2011.14380/) (2020)

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

- [SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery](https://mlsanity.com/paper/2207.08051/) (2022)
- [Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration](https://mlsanity.com/paper/2605.00310/) (2026)

## Abstract (arXiv)

Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing. Unfortunately, these solutions perform best on high-resolution imagery, which is expensive to acquire and infrequently available, making it difficult to scale over long time spans and across large geographies. In this work, we propose a pipeline that leverages a single high-resolution image and a time series of publicly available low-resolution images to generate accurate high-resolution time series for object tracking in urban construction. Our method achieves significant improvement in comparison to baselines using single image super-resolution, and can assist in extending the accessibility and scalability of building construction tracking across the developing world.