ArticleNature communications2021
A generalizable and accessible approach to machine learning with global satellite imagery.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.
- Examining noncommunicable diseases using satellite imagery: a systematic literature review.BMC public health · 2024Pooled it
- A multi-label sentinel-2 dataset for deep learning-based energy, transport, and storage infrastructure mapping in Germany.Data in brief · 2026Article
- A Sentinel-2 dataset for railway presence classification across metropolitan France.Data in brief · 2026Article
- Improving economic impact assessment of climate change with machine learning.Nature communications · 2026Article
- A Global Intra-city Commuting Origin-Destination Flow Dataset for Urban Sustainable Development.Scientific data · 2026Article
- Global high-resolution estimates of the UN Human Development Index using satellite imagery and machine learning.Nature communications · 2026Article
- A high resolution urban and rural settlement map of Africa using deep learning and satellite imagery.Scientific reports · 2026Article
- Deep learning with satellite images enables high-resolution income estimation: A case study of Buenos Aires.PloS one · 2026Article
- Article
- Quantifying Global Foreign Affairs with a Multimodal Dataset of Diplomatic Websites.Scientific data · 2025Article
- Google AI model mines trillions of images to create maps of Earth 'at any place and time'.Nature · 2025Article
- Increasing the resolution of malaria early warning systems for use by local health actors.Malaria journal · 2025Article
- Performance and generalizability impacts of incorporating location encoders into deep learning for dynamic PMGIScience & remote sensing · 2025Article
- Augmenting the availability of historical GDP per capita estimates through machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Inferring the Evolutionary Model of Community-Structuring Traits with Convolutional Kitchen Sinks.Systematic biology · 2024Article
- Cross-prediction-powered inference.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Global poverty estimation using private and public sector big data sources.Scientific reports · 2024Article
- Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.Science (New York, N.Y.) · 2024Article
- Article
- Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.