In a recent article published in the ISPRS International Journal of Geo-Information, Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany, the authors investigated some of the concerns regarding data quality and data usability often levelled at Volunteered Geographic Information (VGI) data sources.
The objective of the study, based in the Rhine-Neckar region of southern Germany, was to compare OSM data to the authoritative land use and land cover (LULC) data set ATKIS Base DLM version 6.0. published by the LGL mapping agency (Baden-Württemberg State Office for Geoinformation and State development).
The results for the OSM data completeness and correctness comparison were variable across the different classes of land use in the study area. However some general trends emerged including:
- Areas with a high percentage of forest cover were the areas with the highest level of completeness and correctness.
- Other classes (incl. farmland and urban areas) had low levels of completeness but higher levels of correctness; features present were mapped accurately but some features were missing.
- Other areas (incl. quarry and lakes) had high levels of completeness (most features mapped) but had a greater percentage of incorrectly mapped features.
- There was a marked difference between rural and urban areas; the study identified higher OSM coverage and thematic accuracy in densely populated areas (more people available/interested in collecting the data?).
- Some land use classes demonstrated both high levels of completeness and correctness, suggesting they had been mapped for a specific purpose.
Although not intended as a definitive statement of OSM data quality, the study suggested that if full coverage and accurate LULC data was a requirement for a project, then OSM data (at present) may not be the best option. However for certain land use classes, where the LULC information was available it was mostly correct so depending on project requirements OSM data may be a suitable alternative.
As we’ve said many times before on Spatial Reserves, it is not whether the data are good, but rather if they are good enough to meet your requirements.
Dorn, H.,Törnros, T. and Zipf, A. (2015). Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany. ISPRS Int. J. Geo-Inf. 4, pp. 1657 – 1670
Recent updates to Esri’s ArcGIS Editor for OpenStreetMap (OSM) add-on and a new OSM edit option in GitHub highlight the continuing popularity of OSM as one of the go-to base layers for many online mapping applications. In April this year, Esri announced the release of ArcGIS Editor for OSM 10.3x, providing an updated free and open source desktop toolset to download, edit and publish updates to OSM.
Two years ago we wrote about the then new option to upload and visualise geoJSON format spatial data in GitHub against a base map provided by OSM. GitHub have now extended the options for viewing and collaborating on spatial data sets to include the base map itself, with a new option to improve the underlying map for registered GitHub and OSM users.
Registered users can either edit the base map themselves or for those who haven’t registered with OSM, leave a note for another editor to review and resolve. Use of the OSM data remains subject to the terms and conditions of the Open Data Common Open Data Licence.
Last year we wrote about the imminent influx of high resolution imagery from unmanned aerial vehicles (UAVs) or drones and the great potential this could offer those agencies responding to emergency situations where the effective provision of humanitarian aid relies heavily on access to current, accurate and readily available map data.
When Typhoon Haiyan (Yolanda), reportedly the strongest typhoon to ever make landfall, struck the Philippines on the 8th of November 2013 it caused catastrophic destruction and loss of life. The Humanitarian OpenStreetMap Team (H.O.T) activated Project Haiyan to provide geographic base data for the affected areas.
However as Kate Chapman reported in a project update last month, although a large number of UAVs had been used to collect imagery immediately after the typhoon struck, much of the mapping activity was uncoordinated, resulting in fragmented data sources that were unavailable to the aid agencies. Although UAV imagery can provide much higher resolution data (5-10cm) than is currently available from satellite imagery sources (0.5m), if the data can’t be accessed when required, the relevant agencies don’t know what’s available and from whom or the licensing arrangements prohibit open access to the data, then the transient opportunities to put the data to good use are lost.
Given the increasing miniaturisation, reduced costs and availability of these devices, a register of publicly available UAV data sources, a crowdsourced OpenUAVImagery initiative or the “OpenReconstruction/Open Drone” platform described by the H.O.T. would seem to be the next step towards making the most of this data resource.