Object Recognition for Economic Development from Daytime Satellite Imagery

Klaus Ackermann (), Alexey Chernikov, Nandini Anantharama, Miethy Zaman and Paul Raschky
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Klaus Ackermann: SoDa Laboratories, Monash University
Alexey Chernikov: SoDa Laboratories, Monash University
Nandini Anantharama: SoDa Laboratories, Monash University
Miethy Zaman: SoDa Laboratories, Monash University

No 2020-02, SoDa Laboratories Working Paper Series from Monash University, SoDa Laboratories

Abstract: Reliable data about the stock of physical capital and infrastructure in developing countries is typically very scarce. This is particular a problem for data at the subnational level where existing data is often outdated, not consistently measured or coverage is incomplete. Traditional data collection methods are time and labor-intensive costly which often prohibits developing countries from collecting this type of data. This paper proposes a novel method to extract infrastructure features from high-resolution satellite images. We collected high-resolution satellite images for 5 million 1km x 1km grid cells covering 21 African countries. We contribute to the growing body of literature in this area by training our machine learning algorithm on ground-truth data. We show that our approach strongly improves the predictive accuracy. Our methodology can build the foundation to then predict subnational indicators of economic development for areas where this data is either missing or unreliable.

Keywords: satellite data; machine learning; physical capital; economic development; africa (search for similar items in EconPapers)
JEL-codes: C55 O18 R11 (search for similar items in EconPapers)
Date: 2020-09
New Economics Papers: this item is included in nep-big and nep-dev
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Working Paper: Object Recognition for Economic Development from Daytime Satellite Imagery (2020)
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