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- Ubukawa Taro
- Geospatial Information Authority of Japan
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- de Sherbinin Alex
- Center for International Earth Science Information Network (CIESIN), Columbia University
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- Onsrud Harlan
- School of Computing and Information Science, University of Maine
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- Nelson Andy
- International Rice Research Institute
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- Payne Karen
- Information Technology Outreach Services, University of Georgia
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- Cottray Olivier
- Geneva International Centre for Humanitarian Demining
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- Maron Mikel
- Open Street Map Foundation
抄録
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.
収録刊行物
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- Data Science Journal
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Data Science Journal 13 (0), 45-66, 2014
CODATA
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詳細情報
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- CRID
- 1390282680211790080
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- NII論文ID
- 130004942287
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- ISSN
- 16831470
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可