# Lightweight and Robust Representation of Economic Scales from Satellite Imagery

arXiv: https://arxiv.org/abs/1912.08197 · HTML: https://mlsanity.com/paper/1912.08197/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 15 of 15)

- [UrbanCLIP: Learning Text-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining from the Web](https://mlsanity.com/paper/2310.18340/) (2023)
- [Knowledge-infused Contrastive Learning for Urban Imagery-based Socioeconomic Prediction](https://mlsanity.com/paper/2302.13094/) (2023)
- [Generalizable Slum Detection from Satellite Imagery with Mixture-of-Experts](https://mlsanity.com/paper/2511.10300/) (2025)
- [A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities](https://mlsanity.com/paper/2308.00465/) (2023)
- [Jane Jacobs in the Sky: Predicting Urban Vitality with Open Satellite Data](https://mlsanity.com/paper/2102.00848/) (2021)
- [Land Cover Mapping in Limited Labels Scenario: A Survey](https://mlsanity.com/paper/2103.02429/) (2021)
- [Estimating On-road Transportation Carbon Emissions from Open Data of Road Network and Origin-destination Flow Data](https://mlsanity.com/paper/2402.05153/) (2024)
- [Devil in the Landscapes: Inferring Epidemic Exposure Risks from Street View Imagery](https://mlsanity.com/paper/2311.09240/) (2023)
- [Satellites Reveal Mobility: A Commuting Origin-destination Flow Generator for Global Cities](https://mlsanity.com/paper/2505.15870/) (2025)
- [UrbanMoE: A Sparse Multi-Modal Mixture-of-Experts Framework for Multi-Task Urban Region Profiling](https://mlsanity.com/paper/2601.22746/) (2026)
- [GeoReg: Weight-Constrained Few-Shot Regression for Socio-Economic Estimation using LLM](https://mlsanity.com/paper/2507.13323/) (2025)
- [CoST: Semantic-Aware Urban Understanding via Spatial-Temporal Alignment](https://mlsanity.com/paper/2608.21041/) (2026)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [Incorporating High-Frequency Weather Data into Consumption Expenditure Predictions](https://mlsanity.com/paper/2211.01406/) (2022)
- [OpenCarbon: A Contrastive Learning-based Cross-Modality Neural Approach for High-Resolution Carbon Emission Prediction Using Open Data](https://mlsanity.com/paper/2506.03224/) (2025)

## Abstract (arXiv)

Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas. While super resolution satellite images are rapidly becoming available, little study has focused on how to extract meaningful information about human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn critical spatial characteristics of arbitrary size areas and represent them into a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates the model outperforms the state-of-the-art in predicting economic scales, such as population density for South Korea (R^2=0.9617), and shows a high potential use for developing countries where district-level economic scales are not known.