The quantification of markers of economic development from time-series satellite imagery using deep learning
Repository Usage Stats
Archival satellite imagery contains massive quantities of largely untapped, objective data documenting the development of nations over time. A key obstacle to leveraging these images for the purposes of advancing population science research has been the lack of systematic methods for quantifying the visually observable changes in a manner that scales efficiently. This paper succeeds in quantifying economically relevant features pertaining to building development, such as square footage and geographical location, from satellite images spanning the years 2005-2009 with an F1-score of 0.8081 in a particularly challenging classification setting. We implement a principled image preprocessing pipeline and a version of the SegNet convolutional neural network architecture described by Badrinarayanan et al. (2016).
ProvenanceUploaded PDF with minor corrections at request of author on 2018-05-01 by mjf33. Uploaded version 3 with minor corrections at request of author on 2018-05-02 by mjf33.
CitationPeshkin, Eric (2018). The quantification of markers of economic development from time-series satellite imagery using deep learning. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/16532.
More InfoShow full item record
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Undergraduate Honors Theses and Student papers