Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria
| العنوان: | Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria |
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| المؤلفون: | Yuan, Jiangye, Chowdhury, Pranab Kanti Roy, McKee, Jacob, Yang, Hsiuhan Lexie, Weaver, Jeanette E., Bhaduri, Budhendra |
| سنة النشر: | 2023 |
| المجموعة: | SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
| مصطلحات موضوعية: | 99 GENERAL AND MISCELLANEOUS |
| الوصف: | 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 km 2 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. |
| نوع الوثيقة: | other/unknown material |
| وصف الملف: | application/pdf |
| اللغة: | unknown |
| Relation: | http://www.osti.gov/servlets/purl/1607293; https://www.osti.gov/biblio/1607293; https://doi.org/10.1038/sdata.2018.217 |
| DOI: | 10.1038/sdata.2018.217 |
| الاتاحة: | http://www.osti.gov/servlets/purl/1607293 https://www.osti.gov/biblio/1607293 https://doi.org/10.1038/sdata.2018.217 |
| رقم الانضمام: | edsbas.A0C38F8 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/sdata.2018.217 |
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