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Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use

Authors:

Shawn NewsamAuthors Info & Claims

UrbanGIS'17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

Article No.: 4, Pages 1 - 8

https://doi.org/10.1145/3152178.3152182

Published: 07 November 2017 Publication History

Abstract

This paper performs a quantitative comparison of open-source data available on the Internet for the fine-grain mapping of land use. Three points of interest (POI) data sources--Google Places, Bing Maps, and the Yellow Pages--and one volunteered geographic information data source--Open Street Map (OSM)--are compared with each other at the parcel level for San Francisco with respect to a proposed fine-grain land-use taxonomy. The sources are also compared to coarse-grain authoritative data which we consider to be the ground truth. Results show limited agreement among the data sources as well as limited accuracy with respect to the authoritative data even at coarse class granularity. We conclude that POI and OSM data do not appear to be sufficient alone for fine-grain land-use mapping.

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Cited By

Liang XAndris C(2021)Measuring McCities: Landscapes of chain and independent restaurants in the United StatesEnvironment and Planning B: Urban Analytics and City Science10.1177/2399808321101489649:2(585-602)Online publication date: 1-Jun-2021
https://doi.org/10.1177/23998083211014896
Chen WZhou YWu QChen GHuang XYu B(2020)Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, ChinaRemote Sensing10.3390/rs1217280512:17(2805)Online publication date: 29-Aug-2020
https://doi.org/10.3390/rs12172805
Yu YFang FLiu YLi SLuo Z(2020)Urban Land Use Classification Using Street View Images Based on Deep Transfer NetworkUrban Intelligence and Applications10.1007/978-3-030-45099-1_7(83-95)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-45099-1_7
Show More Cited By

Index Terms

Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
2. Networks
  1. Network services
    1. Location based services

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cover image ACM Conferences

UrbanGIS'17: Proceedings of the 3rd ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics

November 2017

118 pages

ISBN:9781450354950

DOI:10.1145/3152178

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 07 November 2017

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SIGSPATIAL'17

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SIGSPATIAL

SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 7 - 10, 2017

CA, Redondo Beach, USA

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Cited By

Liang XAndris C(2021)Measuring McCities: Landscapes of chain and independent restaurants in the United StatesEnvironment and Planning B: Urban Analytics and City Science10.1177/2399808321101489649:2(585-602)Online publication date: 1-Jun-2021
https://doi.org/10.1177/23998083211014896
Chen WZhou YWu QChen GHuang XYu B(2020)Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, ChinaRemote Sensing10.3390/rs1217280512:17(2805)Online publication date: 29-Aug-2020
https://doi.org/10.3390/rs12172805
Yu YFang FLiu YLi SLuo Z(2020)Urban Land Use Classification Using Street View Images Based on Deep Transfer NetworkUrban Intelligence and Applications10.1007/978-3-030-45099-1_7(83-95)Online publication date: 26-Jun-2020
https://doi.org/10.1007/978-3-030-45099-1_7
Zhu YDeng XNewsam S(2019)Fine-Grained Land Use Classification at the City Scale Using Ground-Level ImagesIEEE Transactions on Multimedia10.1109/TMM.2019.289199921:7(1825-1838)Online publication date: Jul-2019
https://doi.org/10.1109/TMM.2019.2891999

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