Deep learning with satellite images enables high-resolution income estimation: A case study of Buenos Aires
Nicolás F. Abbate, Leonardo Gasparini, Franco Ronchetti, Facundo M. Quiroga, Krishna Vadrevu, Beata Calka, Beata Calka, Beata Calka
TL;DR
This paper shows how satellite images and deep learning can create detailed income maps, helping policymakers in Buenos Aires and beyond.
Contribution
The study introduces a high-resolution income estimation model using satellite imagery and machine learning.
Findings
The model achieved an R2 score of 0.878 in predicting household incomes in Buenos Aires.
The spatial resolution of the model is over 20 times finer than existing methods.
The approach can generate income maps for any time period using arbitrary satellite images.
Abstract
High-resolution income data is crucial for informing policy decisions as it allows policymakers to better understand the distribution of wealth and poverty. However, obtaining this information is often cost-prohibitive, especially in developing countries. We evaluate the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We train a neural network with satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census data to estimate per capita income at a 50x50 meter resolution for 2013, 2018 and 2022. The model, based on the EfficientNetV2 architecture, demonstrates strong predictive accuracy for household incomes (R2 = 0.878), achieving a spatial resolution over 20 times finer than existing methods in the literature. The model also allows estimating income maps for…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImpact of Light on Environment and Health · Income, Poverty, and Inequality · Remote-Sensing Image Classification