Name
Abstract | ||
|---|---|---|
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is possible to measure local-level economic livelihoods using high-resolution satellite imagery. However, such imagery is relatively expensive to acquire, often not updated frequently, and is mainly available for recent years. We train CNN models on free and publicly available multispectral daytime satellite images of the African continent from the Landsat 7 satellite, which has collected imagery with global coverage for almost two decades. We show that despite these imagesu0027 lower resolution, we can achieve accuracies that exceed previous benchmarks. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Machine Learning | Poverty,Satellite,Satellite imagery,Remote sensing,Multispectral image,Artificial intelligence,Geography,Machine learning |
DocType | Volume | Citations |
Journal | abs/1711.03654 | 0 |
PageRank | References | Authors |
0.34 | 3 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anthony Perez | 1 | 1 | 3.06 |
| Christopher Yeh | 2 | 0 | 1.01 |
| George Azzari | 3 | 34 | 3.87 |
| Marshall Burke | 4 | 26 | 7.40 |
| David B. Lobell | 5 | 38 | 9.70 |
| Stefano Ermon | 6 | 726 | 78.25 |