There is a clear need for a public domain data set of road networks with high special accuracy and global coverage for a range of applications. The Global Roads Open Access Data Set (gROADS), version 1, is a first step in that direction. gROADS relies on data from a wide range of sources and was developed using a range of methods. Traditionally, map development was highly centralized and controlled by government agencies due to the high cost or required expertise and technology. In the past decade, however, high resolution satellite imagery and global positioning system (GPS) technologies have come into wide use, and there has been significant innovation in web services, such that a number of new methods to develop geospatial information have emerged, including automated and semi-automated road extraction from satellite/aerial imagery and crowdsourcing. In this paper we review the data sources, methods, and pros and cons of a range of road data development methods: heads-up digitizing, automated/semi-automated extraction from remote sensing imagery, GPS technology, crowdsourcing, and compiling existing data sets. We also consider the implications for each method in the production of open data.
Ubukawa T, de Sherbinin A, Onsrud H, Nelson A, Payne K, Cottray O, et al.. A Review of Roads Data Development Methodologies. Data Science Journal. 2014;13:45–66. DOI: http://doi.org/10.2481/dsj.14-001
Ubukawa, T., de Sherbinin, A., Onsrud, H., Nelson, A., Payne, K., Cottray, O., & Maron, M. (2014). A Review of Roads Data Development Methodologies. Data Science Journal, 13, 45–66. DOI: http://doi.org/10.2481/dsj.14-001
Ubukawa, Taro, Alex de Sherbinin, Harlan Onsrud, Andy Nelson, Karen Payne, Olivier Cottray, and Mikel Maron. 2014. A Review of Roads Data Development Methodologies 13: 45–66. DOI: http://doi.org/10.2481/dsj.14-001