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Satellite images and machine learning can identify remote communities to facilitate access to health services.

ABSTRACT: OBJECTIVE:Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning. MATERIALS AND METHODS:We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trained an algorithm to detect individual buildings, then examined building clusters to identify groupings suggestive of communities. The approach was validated in southeastern Liberia, by comparing algorithmically generated results with community location data collected manually by enumerators and community health workers. RESULTS:The deep learning approach achieved 86.47% positive predictive value and 79.49% sensitivity with respect to individual building detection. The approach identified 75.67% (n?=?451) of communities registered through the community enumeration process, and identified an additional 167 potential communities not previously registered. Several instances of false positives and false negatives were identified. DISCUSSION:Analysis of satellite images is a promising solution for mapping remote communities rapidly, and with relatively low costs. Further research is needed to determine whether the communities identified algorithmically, but not registered in the manual enumeration process, are currently inhabited. CONCLUSIONS:To our knowledge, this study represents the first effort to apply image recognition algorithms to rural healthcare delivery. Results suggest that these methods have the potential to enhance community health worker scale-up efforts in underserved remote communities.

SUBMITTER: Bruzelius E

PROVIDER: S-EPMC7647253 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Satellite images and machine learning can identify remote communities to facilitate access to health services.

Bruzelius Emilie E Le Matthew M Kenny Avi A Downey Jordan J Danieletto Matteo M Baum Aaron A Doupe Patrick P Silva Bruno B Landrigan Philip J PJ Singh Prabhjot P

Journal of the American Medical Informatics Association : JAMIA 20190801 8-9

<h4>Objective</h4>Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning.<h4>Materials and methods</h4>We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trai ...[more]

PMID: 31411691

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Project description:Annual soil moisture estimates are useful to characterize trends in the climate system, in the capacity of soils to retain water and for predicting land and atmosphere interactions. The main source of soil moisture spatial information across large areas (e.g., continents) is satellite-based microwave remote sensing. However, satellite soil moisture datasets have coarse spatial resolution (e.g., 25-50 km grids); and large areas from regional-to-global scales have spatial information gaps. We provide an alternative approach to predict soil moisture spatial patterns (and associated uncertainty) with higher spatial resolution across areas where no information is otherwise available. This approach relies on geomorphometry derived terrain parameters and machine learning models to improve the statistical accuracy and the spatial resolution (from 27km to 1km grids) of satellite soil moisture information across the conterminous United States on an annual basis (1991-2016). We derived 15 primary and secondary terrain parameters from a digital elevation model. We trained a machine learning algorithm (i.e., kernel weighted nearest neighbors) for each year. Terrain parameters were used as predictors and annual satellite soil moisture estimates were used to train the models. The explained variance for all models-years was >70% (10-fold cross-validation). The 1km soil moisture grids (compared to the original satellite soil moisture estimates) had higher correlations (improving from r2 = 0.1 to r2 = 0.46) and lower bias (improving from 0.062 to 0.057 m3/m3) with field soil moisture observations from the North American Soil Moisture Database (n = 668 locations with available data between 1991-2013; 0-5cm depth). We conclude that the fusion of geomorphometry methods and satellite soil moisture estimates is useful to increase the spatial resolution and accuracy of satellite-derived soil moisture. This approach can be applied to other satellite-derived soil moisture estimates and regions across the world.

Dataset Information

Satellite images and machine learning can identify remote communities to facilitate access to health services.

Publications

Satellite images and machine learning can identify remote communities to facilitate access to health services.

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