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Toward Model-Generated Household Listing in Low- and Middle-Income Countries Using Deep Learning

RTI International, Research Triangle Park, NC 27709, USA
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ISPRS Int. J. Geo-Inf. 2018, 7(11), 448; https://doi.org/10.3390/ijgi7110448
Received: 19 September 2018 / Revised: 7 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
(This article belongs to the Special Issue Geo-Information and the Sustainable Development Goals (SDGs))
While governments, researchers, and NGOs are exploring ways to leverage big data sources for sustainable development, household surveys are still a critical source of information for dozens of the 232 indicators for the Sustainable Development Goals (SDGs) in low- and middle-income countries (LMICs). Though some countries’ statistical agencies maintain databases of persons or households for sampling, conducting household surveys in LMICs is complicated due to incomplete, outdated, or inaccurate sampling frames. As a means to develop or update household listings in LMICs, this paper explores the use of machine learning models to detect and enumerate building structures directly from satellite imagery in the Kaduna state of Nigeria. Specifically, an object detection model was used to identify and locate buildings in satellite images. In the test set, the model attained a mean average precision (mAP) of 0.48 for detecting structures, with relatively higher values in areas with lower building density (mAP = 0.65). Furthermore, when model predictions were compared against recent household listings from fieldwork in Nigeria, the predictions showed high correlation with household coverage (Pearson = 0.70; Spearman = 0.81). With the need to produce comparable, scalable SDG indicators, this case study explores the feasibility and challenges of using object detection models to help develop timely enumerated household lists in LMICs. View Full-Text
Keywords: sustainable development goals (SDGs); machine learning; object detection; household enumeration; survey statistics; remote sensing sustainable development goals (SDGs); machine learning; object detection; household enumeration; survey statistics; remote sensing
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Chew, R.; Jones, K.; Unangst, J.; Cajka, J.; Allpress, J.; Amer, S.; Krotki, K. Toward Model-Generated Household Listing in Low- and Middle-Income Countries Using Deep Learning. ISPRS Int. J. Geo-Inf. 2018, 7, 448.

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