Osm Quality Assessment 2008

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Muki Haklay

OSM Quality Assessment - presented in S4 event, London, 8th Jan 2008

Outline ,[object Object],[object Object],[object Object],[object Object]

User Generated Geographical information ,[object Object],[object Object],[object Object]

Geograph.org.uk ,[object Object],[object Object],[object Object]

OpenStreetMap ,[object Object],[object Object],[object Object],[object Object],(Image source: OpenStreetMap)

OpenStreetMap ,[object Object],[object Object]

Creating Maps for OSM (Image source: OpenStreetMap)

OSM technological stack (cc) OpenStreetMap Haklay, M. And Weber, P., 2008, OpenStreetMap – User Generated Street Map, IEEE Pervasive Computing.

Simplified glue – OSM API vs. OGC WMS ,[object Object],[object Object],[object Object],Haklay, M. And Weber, P., 2008, OpenStreetMap – User Generated Street Map, IEEE Pervasive Computing.

Mapping parties (cc) Urbanwide - flickr (cc) Nick black (c) Andrea Antonello

Achievements ,[object Object],[object Object],[object Object]

The quality issue ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The problem ,[object Object],[object Object]

Who collects? (c) Dair Grant (cc) Chris Fleming (cc) Shaun McDonald

Number of Users Area covered (Sq Km) 1 40021 2 20720 3 9136 4 4184 5 1986 6 936 7 448 8 269 9 139 10 and above 246

Accuracy and Completeness ,[object Object],[object Object],[object Object]

Positional Accuracy ,[object Object],A B

Goodchild and Hunter (1997), Hunter (1999) method ,[object Object],[object Object],[object Object]

Motorway comparison ,[object Object],[object Object],Motorway Percentage Overlap M1 87.36% M2 59.81% M3 71.40% M4 84.09% M4 Spur 88.77% M10 64.05% M11 84.38% M20 87.18% M23 88.78% M25 88.80% M26 83.37% M40 72.78% A1(M) 85.70% A308(M) 78.27% A329(M) 72.11% A404 76.65%

Positional accuracy ,[object Object],[object Object],Area Average difference (m) Barnet 6.77 Highgate 8.33 New Cross 6.04 South Norwood 3.17 Sutton 4.83 Total 5.83

Completeness – bulk method ,[object Object],[object Object],[object Object]

Length comparison ,[object Object],[object Object],[object Object]

So should I use OSM? ,[object Object],[object Object],[object Object]

Conclusions ,[object Object],[object Object],[object Object]

Further reading ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

We show how deep learning can be effectively applied to remote sensing. Many problems we faced, solutions we have had discovered were highlighted too. Remotely sensed data, unlike other vision tasks are very challenging and posses extra difficulties. Objects are very small compared to the image size, and even small pixel sizes of 8*10 pixel can contain huge amount of informations. To the best of our knowledge there is no automated or simi-automated tool that uses deep learning to detect features from satellite imagery.

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The recent increase in the availability of spaceborne radar in different wavelengths with multiple polarisations provides new opportunities for land surface analysis. This research effort explored how different radar data, and derived texture values, independently and in combination with optical imagery influence land cover/use classification accuracies for a study site in Washington, DC, USA. Two spaceborne radar images, Radarsat-2L-band and Palsar C-band quad-polarised radar, were registered with Aster optical data for this study. Traditional methods of classification were applied to various components and combinations of this data set, and overall and class-specific thematic accuracies obtained for comparison. The results for the two despeckled radar data sets were quite different, with Radarsat-2 obtaining an overall accuracy of 59% and Palsar 77%, while that of the optical Aster was 90%. Combining the original radar and a variance texture measure increased the accuracy of Radarsat-2 to 71% but that of Palsar only to 78%. One of the sensor fusions of optical and radar obtained an accuracy of 93%. For this location, radar by itself does not obtain classification accuracies as high as optical data, but fusion with optical imagery provides better overall thematic accuracy than the optical independently, and results in some useful improvements on a class-by-class basis. For those regions with high cloud cover, quad polarisation radar can independently provide viable results but it may be wavelength-dependent.

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The role of learning in citizen science

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This is a presentation from the citizen science impact event at the Open University http://www.open.ac.uk/blogs/opentel/citizen-science-impact-event-at-the-open-university/ Citizen science offer different levels of engagement to participants, which have been captured in typologies of the field (contributory, collaborative, co-created, collegial / crowdsourcing, distributed intelligence, participatory science, extreme citizen science). These typologies do no explicitly examine learning. At the same time, projects and activities striving to fulfil multiple goals (excellent scientific output, satisfying engagement, good recruitment, learning …). Within ythe range of citizen science project, we can consider different aspects of learning that are occurring in them, Projects and use examples from a range of project, and raise some aspects that can help those who are designing co-created projects.

The Willing Volunteer – Incorporating Voluntary Data into National Databases

Muki Haklay

Examining the values that are embedded in the processes and technologies of p...

Muki Haklay

A persistent question about participatory methodologies that rely on technologies, such as public participation geographic information systems (PPGIS), is how to integrate values, such as inclusiveness of all the people that are impacted by a decision, or identifying options that are popular by the majority but acceptable to the minority, within technologically focused projects. Moreover, technologies do not operate by themselves – they are embedded in organizational, political, and social processes that set how they are used, who can use them, and in what context. Therefore, we should explore where the values reside? Two factors obscure our view: The misleading conceptualisation that technologies are value free, and can be used for good or for bad – which put all the weight on the process, and ignores the way in which any technology allow only certain actions to be taken. Another popular view of technology conceptualisation is to emphasise their advantages (upside) and ignore their limitations. If we move beyond these, and other “common sense” views of technologies, we can notice how process and technology intertwine. We can therefore look at the way the process/technology reinforce and limit each other, and the way that the values are integrated and influence them. With this analysis, we can also consider how technological development can explicitly include considerations of values, and be philosophically, politically, and social-theory informed. We need to consider the roles, skills, and knowledge of the people that are involved in each part of the process – from community facilitation to software development. The paper will draw on the experience of developing participatory geographic information technologies over the past 20 years, and will suggest future directions for values-based participatory technology development.

Into the Night - Technology for citizen science

Muki Haklay

Current citizen science seems effortless...just download an app and start using it. However, there are many technical aspects that are necessary to make a citizen science project work. In this session, we will provide an overview of all the technical elements that are required - from the process of designing an app., to designing and managing a back-end system, to testing the system end to end before deployment. Participants will have the opportunity to engage in a short exercise to consider the design of an app for a citizen science project that addresses light pollution.

Into the Night - Citizen Science Training day - introduction to citizen science

Muki Haklay

Setting, running and evaluating - In this session, we will provide a brief overview of the types of citizen science that are relevant in addressing environmental challenges. We will look at classifications of citizen science projects, explore their potential goals, the process of recruitment and retention as well as the need to start project evaluation from an early stage. Participants will have the opportunity to engage in a short exercise to consider how these elements can be used in the design of a citizen science project.

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