NDLI: Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery

Content Provider	Springer Nature : BioMed Central
Author	Chew, Robert F. Amer, Safaa Jones, Kasey Unangst, Jennifer Cajka, James Allpress, Justine Bruhn, Mark
Abstract	Background Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative sample. Geosampling is a probability-based, gridded population sampling method that addresses some of these issues by using geographic information system (GIS) tools to create logistically manageable area units for sampling. GIS grid cells are overlaid to partition a country’s existing administrative boundaries into area units that vary in size from 50 m × 50 m to 150 m × 150 m. To avoid sending interviewers to unoccupied areas, researchers manually classify grid cells as “residential” or “nonresidential” through visual inspection of aerial images. “Nonresidential” units are then excluded from sampling and data collection. This process of manually classifying sampling units has drawbacks since it is labor intensive, prone to human error, and creates the need for simplifying assumptions during calculation of design-based sampling weights. In this paper, we discuss the development of a deep learning classification model to predict whether aerial images are residential or nonresidential, thus reducing manual labor and eliminating the need for simplifying assumptions. Results On our test sets, the model performs comparable to a human-level baseline in both Nigeria (94.5% accuracy) and Guatemala (96.4% accuracy), and outperforms baseline machine learning models trained on crowdsourced or remote-sensed geospatial features. Additionally, our findings suggest that this approach can work well in new areas with relatively modest amounts of training data. Conclusions Gridded population sampling methods like geosampling are becoming increasingly popular in countries with outdated or inaccurate census data because of their timeliness, flexibility, and cost. Using deep learning models directly on satellite images, we provide a novel method for sample frame construction that identifies residential gridded aerial units. In cases where manual classification of satellite images is used to (1) correct for errors in gridded population data sets or (2) classify grids where population estimates are unavailable, this methodology can help reduce annotation burden with comparable quality to human analysts.
Related Links	https://ij-healthgeographics.biomedcentral.com/counter/pdf/10.1186/s12942-018-0132-1.pdf
Ending Page	17
Page Count	17
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s12942-018-0132-1
Journal	International Journal of Health Geographics
Issue Number	1
Volume Number	17
Language	English
Publisher	BioMed Central
Publisher Date	2018-05-09
Access Restriction	Open
Subject Keyword	Public Health Health Informatics Human Geography Epidemiology Health Promotion and Disease Prevention Medical Geography Machine learning Deep learning Scene classification Probability based Complex sample design Clustering Remote sensing GIS
Content Type	Text
Resource Type	Article
Subject	Computer Science Public Health, Environmental and Occupational Health Business, Management and Accounting
Journal Impact Factor	3/2023
5-Year Journal Impact Factor	4.1/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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