Neal Jean, " "Combining satellite imagery and machine learning to predict poverty"

Neal Jean, Michael Xie, Stefano Ermon, Matt Davis, Marshall Burke, David Lobell
"Combining satellite imagery and machine learning to predict poverty"
Stanford University Depts of Computer Science and Earth Systems Science
CompSust-2016
4th International Conference on
Computational Sustainability
July 7, 2016
3:30 PM, Machine and Statistical Learning for Conservation, Poverty, Energy, and Climate
www.compsust.ne...

[@azzakialjaylani7679]

Good presentation and material

[@sabeerawa05]

Wow! superb work... Please can you help with the research materials such as Algorithms and codes?? Thanks.....

[@asusinger]

yeah, it better be complimentary as you said . This is really not something what can predict the infrastructure vs poverty gap. Also it will not help in derivitations like GDP or precise income levels and expenditure ratio . Or the extreme poverty vs. Lack of access.

[@ibrahimmuhammed5332]

Thanks for sharing! But which satellite imagery are you gonna use? Likely commercial multispectral high resolution 30cm could do.

[@antonixu2773]

Hi, I wonder if you provide any research guideline to reproduce your work, if any where can I read it? Thank You

[@incendioraven4269]

he has a github account and related code there

[@sabeerawa05]

@@incendioraven4269 Please can you help with his github account?