Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria

Yuan, Jiangye; Chowdhury, Pranab Kanti Roy; McKee, Jacob; Yang, Hsiuhan Lexie; Weaver, Jeanette E.; Bhaduri, Budhendra

doi:10.1038/sdata.2018.217

Title: Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria

Journal Article · Tue Oct 23 00:00:00 EDT 2018 · Scientific Data

DOI:https://doi.org/10.1038/sdata.2018.217· OSTI ID:1607293

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)

Buildings in the developing world are inadequately mapped. Lack of such critical geospatial data adds unnecessary challenges to locating and reaching a large segment of the world’s most vulnerable population, impeding sustainability goals ranging from disaster relief to poverty reduction. Use of volunteered geographic information (VGI) has emerged as a widely accepted source to fill such voids. Despite its promise, availability of building maps for developing countries significantly lags behind demand. We present a new approach, coupling deep convolutional neural networks (CNNs) with VGI for automating building map generation from high-resolution satellite images for Kano state, Nigeria. Specifically, we trained a CNN with VGI building outlines of limited quality and quantity and generated building maps for a 50,000 km² area. Resulting maps are in strong agreement with existing settlement maps and require a fraction of the manual input needed for the latter. The VGI-based maps will provide support across multiple facets of socioeconomic development in Kano state, and demonstrates potential advancements in current mapping capabilities in resource constrained countries.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; Bill and Melinda Gates Foundation

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1607293

Journal Information:: Scientific Data, Vol. 5, Issue 1; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 14 works

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The impact of seasonality on multi-scale feature extraction techniques Moore, Brian; McKee, Jacob J. International Journal of Digital Earth, Vol. 13, Issue 1 https://doi.org/10.1080/17538947.2018.1556354	journal	January 2019
An overview of GeoAI applications in health and healthcare Kamel Boulos, Maged N.; Peng, Guochao; VoPham, Trang International Journal of Health Geographics, Vol. 18, Issue 1 https://doi.org/10.1186/s12942-019-0171-2	journal	May 2019
Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1 Melchiorri, Michele; Pesaresi, Martino; Florczyk, Aneta ISPRS International Journal of Geo-Information, Vol. 8, Issue 2 https://doi.org/10.3390/ijgi8020096	journal	February 2019
Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep Learning Herfort, Benjamin; Li, Hao; Fendrich, Sascha Remote Sensing, Vol. 11, Issue 15 https://doi.org/10.3390/rs11151799	journal	July 2019
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