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

ABSTRACT: 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

SUBMITTER: Yuan J 

PROVIDER: S-EPMC6198754 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature