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Intrinsic OSM data quality assessment The intrinsic quality assessment of building footprints data on OpenStreetMap in Baden- Württemberg

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In this work, we propose a framework to assess the quality of OSM building footprints data without using any reference data. More specifically, the OSM history data will be examined regarding the development of attributes, geometries and positions of building footprints. In total seven quality indicators are defined for the intrinsic quality assessment. For our case study in the federal state of Baden-Württemberg (BW), Germany, a PostgreSQL database is established based on a spatiotemporal data model which can track both individual objects and editing events on OSM. The preliminary experiments show that the quality of building footprints in BW is relatively high. And the quality in terms of semantics, geometries and positions are getting increasingly high over the time thanks to the considerable contribution of OSM volunteers.
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Intrinsic OSM data quality assessment
1
The intrinsic quality assessment of building
footprints data on OpenStreetMap in Baden-
Württemberg
Hongchao Fan, Anran Yang, and Alexander Zipf
Summary
In this work, we propose a framework to assess the quality of OSM building
footprints data without using any reference data. More specifically, the OSM history
data will be examined regarding the development of attributes, geometries and
positions of building footprints. In total seven quality indicators are defined for the
intrinsic quality assessment. For our case study in the federal state of Baden-
Württemberg (BW), Germany, a PostgreSQL database is established based on a
spatiotemporal data model which can track both individual objects and editing
events on OSM. The preliminary experiments show that the quality of building
footprints in BW is relatively high. And the quality in terms of semantics, geometries
and positions are getting increasingly high over the time thanks to the considerable
contribution of OSM volunteers.
1 Introduction
OpenStreetMap (OSM) is considered one of the most successful and popular VGI
projects, and it has attracted significant and sustained interest in academia,
industry, and governmental agencies. Currently, there are almost three million
registered members (OSM, 2016), which has led OSM to grow rapidly. With the
rapid development of OSM in recent years, especially, sparked by the availability of
high-resolution imagery from Bing since 2010, there has been an increase in
building information in OSM, proving that volunteers do not only contribute roads or
points of interest (POIs) to the database. According to the latest statistics (the
values are derived from our internal OSM database, which is updated daily), the
number of buildings in OSM is above 200 million, thereof 18.4 million building
footprints in Germany. The research of Fan et al. (2014) demonstrated that the data
regarding building footprints on OSM has a high degree of completeness and
semantic accuracy. There is an offset of about four meters on average in terms of
position accuracy. With respect to shape, OSM building footprints have high
similarity to ATKIS (the German authority data) footprint data. Moreover, there is
more and more information about building heights and roof structures, which is
required for the 3D reconstruction.
Because the data are collected through crowd-sourcing, OSM is often doubted
regarding data quality. In 2013, Kunze and her colleagues applied several methods
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H. Fan, A. Yang & A. Zipf
to assess the completeness of the building information in OSM in comparison to an
administrative dataset for two federal states in Germany (Kunze et al. 2013; Hecht
et al. 2013). As the criterion of quality assessment, the work mainly analyzed the
area difference of a group of buildings within hexagon/square instead of individual
correspondence. Fan et al. (2014) addressed the OSM building completeness in
Munich, Germany. The authors compared OSM building footprints data with ATKIS
data in terms of completeness, semantic accuracy, position accuracy, and shape
accuracy. Klonner et al. (2015) also addressed the building completeness and
conducted a data quality analysis of building footprints in Bregenz, Austria. Most
recently, authors employ authority datasets to compare the quality of OSM in terms
of the completeness and the thematic accuracy in the dataset (Törnros et al 2015;
Dorn et al 2015).
The abovementioned methods of quality assessment rely on the access of
reference datasets which are unfortunately, in many cases, not available due to
contradictory licensing restrictions or high procurement costs. Therefore, intrinsic
quality assessment has been introduced in the recent years. Many existing
approaches examined OSM data by checking the change history of features. For
example, Keßler and Groot (2013) evaluated feature-level attributes such as the
number of versions, the stability against changes and the corrections and rollbacks
of features so to infer OSM features’ quality. Barron et al. (2014) have developed a
comprehensive analysis framework, called iOSManalyser, for investigating the
intrinsic data quality of OSM based on its mapping history. In their work, a broad
range of more than 25 different methods and indicators were presented to evaluate
the quality of an OSM dataset.
This work is dedicated to the intrinsic quality assessment for building footprints data
on OSM. First of all, a conceptual framework is developed regarding the intrinsic
quality assessment for OSM building footprints data. At the same time, the
preliminary results of the intrinsic quality assessment in Baden-Württemberg,
Germany, will be demonstrated. Furthermore, a conceptual framework for the
effective analysis of OSM history data will be presented in order to carry out the
intrinsic quality assessment for OSM data.
2 The conceptual framework of intrinsic quality
assessment for OSM building footprints data
In total, seven indicators are defined for the intrinsic quality assessment of building
footprints data on OSM:
In terms of completeness:
(i) the development of built-up area over time,
(ii) the development of building count over time,
(iii) the development of positional accuracy attributes over time,
Intrinsic OSM data quality assessment
3
(iv) the average vertex displacement of a building footprint when edited by
OSM contributors,
and in terms of shape accuracy:
(v) the orthogonality of building footprints,
(vi) the parallelism of building footprint edges to the nearby line segments
of roads, and
(vii) the fragmentariness of patterns formed by building footprints.
While the first three indicators and the fifth one are quite obviously and easy to be
understood, the fourth, sixth and seventh indicator will be elaborated in the
following, in order to give a better depiction how they will be calculated.
(1) Calculation of the average displacement of a vertex on a building footprint
polygon after several editing processes on OSM
It is assumed that a vertex   of a building footprint polygon is (re-
)edited many times       by different contributors on OSM.
Due to the non-rectangular shape on the vertex and some other reasons
such as shadow or occlusion by vegetation, the contributors might have
problems to see the exact vertex on the aerial or satellite images. For this
reason, the exact position might be estimated and corrected differently by
different OSM contributors. As the result, there are many records of
positions of the referred vertex. In order to estimate its most likely location,
a grid-based accumulation space is generated at first. Then the number of
points falling within the accumulation cells is counted. The cell with the
largest number is then the most likely location of the vertex. The average
displacement to this point is then the positional accuracy.
(2) Calculation of the parallelism of building footprint edges
The right image of fig.1 shows some buildings in Heidelberg, Germany.
Normally, the edges of buildings immediately adjacent are parallel to the
nearby street. In most cases, building footprints are decomposed into long
line segments, while the street nearby consists also of line segments. In
these cases, the parallelism is very easy to be calculated. However, there
are some cases in which one of the edges of the building footprint is curved
and just like the street nearby as demonstrated on the left image of fig.1. In
this case, the edge of building footprint is denoted as , while the street
nearby is represented as .
In the first step, is converted into a sequence of points (   
)
with small and equal intervals. For point , its foot point , which is
perpendicular to is calculated. If  is located on , the distance
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H. Fan, A. Yang & A. Zipf
from the point to , is calculated as the Euclidean distance from 
and , namely,  . It is assumed that there are points
(   
 ) (        ) on that have foot points
(   ) perpendicular to . The distance between and is
then the RMS (root mean square) of (   ), . The RMSE (root
mean square error)  is then used to evaluate the parallelism of
and . In this work, the distance between two points is set at 0.3 m,
which is sufficiently small compared with a line segment of a road in the
physical world, which can ensure that the RMSE can be used to
evaluate parallelism.
Fig.1 The parallelism of edges of building footprints to road network (the image on
the right figure is taken from Google Map)
(3) Calculation of the fragmentariness of patterns formed by building footprints
Buildings with similar shapes and sizes in the same area can form patterns
if they are distributed regularly. This kind of knowledge can be used for
intrinsic quality assessment of OSM building footprint. Firstly, the building
footprints in a local area will be compared in terms of shapes and sizes,
whereby a turning function is applied for the similarity. Secondly, the
centroids of similar building footprints are used to estimate the distribution
of the buildings by using regression or partition regression models. In the
next step, it will check whether there are buildings intersecting with the
pattern but not considered as similar buildings in the pattern. This building
should share the shape and size to the buildings in the pattern according to
the abovementioned hypothesis. A pseudo building footprint with the same
shape and size will be calculated at its position. And its orientation can be
Intrinsic OSM data quality assessment
5
computed by the interpolation of the pattern regression. Finally, the
positional accuracy and shape accuracy can be calculated by comparing
the OSM building footprint at that location and the pseudo building footprint.
It is also possible to obtain the displacement of the orientation.
3 The Spatiotemporal data model for OSM history data
In order to conduct the intrinsic quality assessment of OSM data, the full history of
OSM data has to be made available. The original format of the OSM history is very
terse and capable to express rich information about the entities, their relationships,
and their temporal changes. The geometry oriented models used by editing tools
cannot provide an equivalent representation of the data. In this work, we developed
a spatiotemporal data model not only for the purpose of assessing building
footprints data, but also for the purpose of a comprehensive investigation of OSM
features in terms of data quality and user behaviour.
We first discuss the pure temporal model of OSM history, namely, entity and event,.
In the so-called Interval Based Model, an entity is homogeneous across the interval
but an event is not, that is, an entity over and the entity over   is the same
entity, which is not the case for an event. For example, a building in 2010-2011 will
be the same building as in 2010-2012, but the improvement of a building from 2010
to 2011 is only part of the improvement from 2010 to 2012. In the Instant Based
Model, the entity and the event are not so distinguishable in theory, but usually we
can recognize them in the context. For example, nodes addedis clearly an event,
while “road 111 at 2016-01-01 00:00" is an entity.
We propose a model including four types, which are entities over intervals, events
over intervals, entities at instants, and events at instants. The objects over intervals
have two essential properties enter_time and exit_time, while objects over
instants have one property named timespot. Granted that there is a function
  calculates the value of the entity at time . The semantic of  is trivial
in the instant based view, but in the interval based view,  is inconclusive unless
 ,  . For convenience, we further define the predicate
 in the interval view as:
       
The definition of  clearly suggests that it depends on how  is
understood, which brings the confusion about versions of ways and relations. If 
is defined as the tags and referred node/member entities, then the result is exactly
the same versions marked in the original data. However, if ways and relations are
regarded as geometry with tags much more versions result. Let it be supposed that
a ”version" of a highway spanning from January 1st to February 3rd, of which one of
its inner nodes N changes its position on January 29th. The “version” is in fact not a
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H. Fan, A. Yang & A. Zipf
version since           . Instead,  in such
case can be defined as:
     

The five fundamental types in OSM history are defined as shown in figure 2. We
preserve most of the original structures with some differences to make the whole
model more consistent and convenient.
Fig. 2 Five major types corresponding to the original data.
4 Preliminary results in Baden-Württemberg
For the current stage, a PostgreSQL database has been established by using the
spatiotemporal model introduced in section 3. We use the building footprints data in
Baden-Württemberg (BW) for the test of the proposed intrinsic quality assessment.
(a)
(b)
(d)
Fig.3 The development of parameters of building footprints in BW
Intrinsic OSM data quality assessment
7
As shown in figure 3, the total area (fig.3a) of building footprints in BW trend
convergently, while the number (fig.3b) of building footprints still seems to rise with a
steep slope. fig.3c depicts that the number of buildings tagged with attributes is still
increasing rapidly. And fig.3d represents the development of the percentage of
buildings with rectangle shape. The valley in 2012 is followed by an increase of the
percentage of rectangle-shaped building footprints. This reflects the reality that
buildings were mapped in blocks as rectangles at the early stage of OSM
development. Then OSM contributors started mapping buildings with complex
shapes. From 2012 onwards, the polygons as group-buildings have been refined, so
that individual buildings are digitized. This development means that both the
semantic and the geometric accuracy of building data on OSM have been improved
in the recent years.
Using the method presented in section 3, the parallelism of edges of building
footprints to the nearby road line segments are calculated. Fig.5 shows that most of
the buildings have an edge which is more than 90 % parallel to the immediately
adjacent street/road. This is almost consistent with reality. In other words, the
positional accuracy of building footprints in BW seems to be very high.
Fig.5 The parallelisms of building footprints edges to their nearby roads (
5 Conclusion
In this paper, a framework is presented for the intrinsic quality assessment of OSM
building footprints data. In total, seven quality indicators are suggested for the
quality measurement without any reference data. The main idea is to observe the
historic development of OSM data on the one hand. On the other hand, we are
utilizing the knowledge in urban area to define indicators of intrinsic quality, because
urban is man-made environment and almost everything is constructed based on a
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H. Fan, A. Yang & A. Zipf
certain rule. For instance, buildings are constructed with rectangles. Then the
rectangularity of building footprints can be used as a kind of indicator for the quality
of building footprint. Preliminary statistics of data on building footprints have been
done by using the OSM history data in Baden-Württemberg, Germany. The
experimental results show that data on building footprints in BW are mapped with
relatively high accuracy and the quality in terms of semantics, geometries and
positions is still improving over time.
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Photogrammetry, Remote Sensing and Spatial Information Sciences, 1, 353-357.
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High availability and diversity make Volunteered Geographic Information (VGI) an interesting source of information for an increasing number of use cases. Varying quality, however, is a concern often raised when it comes to using VGI in professional applications. Recent research directs towards the estimation of VGI quality through the notion of trust as a proxy measure. In this chapter, we investigate which indicators influence trust, focusing on inherent properties that do not require any comparison with a ground truth dataset. The indicators are tested on a sample dataset extracted from OpenStreetMap. High numbers of contributors, versions and confirmations are considered as positive indicators, while corrections and revisions are treated as indicators that have a negative influence on the development of feature trustworthiness. In order to evaluate the trust measure, its results have been compared to the results of a quality measure obtained from a field survey. The quality measure is based on thematic accuracy, topological consistency, and information completeness. To address information completeness as a criterion of data quality, the importance of individual tags for a given feature type was determined based on a method adopted from information retrieval. The results of the comparison between trust assessments and quality measure show significant support for the hypothesis that feature-level VGI data quality can be assessed using a trust model based on data provenance.
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Assessing the completeness of building footprints in OpenStreetMap: an example from Germany
  • C Kunze
  • R Hecht
  • S Hahmann
Kunze, C., Hecht, R., & Hahmann, S. (2013, August). Assessing the completeness of building footprints in OpenStreetMap: an example from Germany. In 26th International Cartographic conference (pp. 25-30).