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Quality based approach for updating geographic authoritative datasets from crowdsourced GPS traces

Abstract : Nowadays, the need for very up to date authoritative spatial data has significantly increased. Thus, to fulfill this need, a continuous update of authoritative spatial datasets is a necessity. This task has become highly demanding in both its technical and financial aspects. In terms of road network, there are three types of roads in particular which are particularly challenging for continuous update: footpath, tractor and bicycle road. They are challenging due to their intermittent nature (e.g. they appear and disappear very often) and various landscapes (e.g. forest, high mountains, seashore, etc.).Simultaneously, GPS data voluntarily collected by the crowd is widely available in a large quantity. The number of people recording GPS data, such as GPS traces, has been steadily increasing, especially during sport and spare time activities. The traces are made openly available and popularized on social networks, blogs, sport and touristic associations' websites. However, their current use is limited to very basic metric analysis like total time of a trace, average speed, average elevation, etc. The main reasons for that are a high variation of spatial quality from a point to a point composing a trace as well as lack of protocols and metadata (e.g. precision of GPS device used).The global context of our work is the use of GPS hiking and mountain bike traces collected by volunteers (VGI traces), to detect potential updates of footpaths, tractor and bicycle roads in authoritative datasets. Particular attention is paid on roads that exist in reality but are not represented in authoritative datasets (missing roads). The approach we propose consists of three phases. The first phase consists of evaluation and improvement of VGI traces quality. The quality of traces was improved by filtering outlying points (machine learning based approach) and points that are a result of secondary human behaviour (activities out of main itinerary). Remained points are then evaluated in terms of their accuracy by classifying into low or high accurate (accuracy) points using rule based machine learning classification. The second phase deals with detection of potential updates. For that purpose, a growing buffer data matching solution is proposed. The size of buffers is adapted to the results of GPS point’s accuracy classification in order to handle the huge variations in VGI traces accuracy. As a result, parts of traces unmatched to authoritative road network are obtained and considered as candidates for missing roads. Finally, in the third phase we propose a decision method where the “missing road” candidates should be accepted as updates or not. This decision method was made in multi-criteria process where potential missing roads are qualified according to their degree of confidence. The approach was tested on multi-sourced VGI GPS traces from Vosges area. Missing roads in IGN authoritative database BDTopo® were successfully detected and proposed as potential updates
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https://tel.archives-ouvertes.fr/tel-01940246
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Submitted on : Friday, November 30, 2018 - 10:24:07 AM
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Stefan Ivanovic. Quality based approach for updating geographic authoritative datasets from crowdsourced GPS traces. Geography. Université Paris-Est, 2018. English. ⟨NNT : 2018PESC1068⟩. ⟨tel-01940246⟩

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