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We cordially invite everybody interested to our next open GIScience colloquium talk

The speaker is Dr. Jack Williams
Department of Geography, Durham University

When: Monday 28.05.2018, 2:15 pm

Where: INF 348, room 015 (Institute of Geography, Heidelberg University)

Near-continuous monitoring of rockfalls and insights into post-seismic landslide patterns

This talk focuses on two strands of research that are both partially underpinned by the importance of using monitoring strategies tailored to the geomorphic change that is under examination. The first section focuses on the improved understanding of rockfall occurrence gained from near-continuous (c. 1 h) LiDAR monitoring of an actively failing coastal rockslope. Current understanding of the nature of rockfall and their controls stems from the capabilities of slope monitoring. These capabilities are fundamentally limited by the frequency and resolution of data that can be captured. An overview of the workflow and, in particular, the practicalities of 4D monitoring is provided. Monitoring at this resolution captures the importance of small rockfalls that ordinarily fail to be discretised due to their superimposition and coalescence, which has important implications for our understanding of the underlying failure mechanisms. Insights into the influence of sub-aerial drivers and the presence of accelerated deformation prior to failure are also presented. The second part of the talk focuses on patterns of landsliding in the years after an earthquake, here the 2015 Gorkha earthquake in Nepal. In addition to triggering ~25,000 coseismic landslides, the earthquake resulted in extensive and pervasive cracking on many hillslopes that did not undergo full coseismic collapse. Monitoring both new and existing landslides is critical for understanding rates of sediment mobilisation, the role of coseismic damage accumulation in driving post-seismic slope failure, and the evolving nature, extent, and severity of landslide risk. Here, initial results are presented from both ground-based monitoring and mapping from medium-resolution satellite imagery. Topographic distributions of new and developing landslides from 2014-2017 are drawn upon to suggest that a return to pre-earthquake landsliding is ongoing.

Last weekend Heidelberg was the host city of this semesters geography “BuFaTa” (Bundesfachschaftentagung). During this four day event student associations from Germany, Austria and Switzerland and their members came together to discuss, learn and spend time together. The event was organized by an excellent student team from Heidelberg.

The disastermappers Heidelberg contributed to the BuFaTa by organising a Missing Maps mapathon as one of the plenty working group activities. During three hours they presented an overview on mapping activities to support disaster management and humanitarian aid. Furthermore, the participants became active themselves by contributing data to MapSwipe and/or OpenStreetMap. By spreading the word about the work of the Humanitarian OpenStreetMap TeamDoctors Without Borders and the Red Cross organisations and the local team at the Heidelberg Institute for Geoinformation Technology (HeiGIT), the goal is to help to establish more mapathons in other German cities organized by local student groups.

A group of motivated students during the mapathon (Foto: Daniel Wagner) https://disastermappers.wordpress.com/

Everyone insterested in organising mapathons at his/her home town is welcome to contact the disastermappers heidelberg. They’re happy to share the experiences gained on the long journey starteing already in 2013 during the mapping efforts after Typhoon Haiyan. Just write an email to disastermappers@posteo.de ( or contact via Twitter or Facebook).

For the ones, who can’t wait to learn more: There already is a list of tutorials and workshop material available under the tutorials section of the disastermappers blog.

This morning Georg Glasze and Thomas Bittner from Erlangen University gave a very interesting keynote talk on “VGI and society: inequalities, exclusions and power asymmetries of user generated geoinformation”. It was well received and lead to a lively discussion among the participants of the DFG SPP VGIscience Collaborative Research Week that is taking place this week at Heidelberg University. Below you find some impressions from the talk.

We are looking forward to the second keynote presentation by Jasper van de Ven (Bremen) on “Thoughts on Privacy” tomorrow Thursday 24 May at 9.00 am. If you are interested feel invited to join us in the Mathematikon, Heidelberg University. INF 205, floor 5. Conference Room.

Our projects within the DFG SPP VisVIG are related to “A framework for measuring the fitness for purpose of OpenStreetMap data based on intrinsic quality indicators” and “Spatial Correlation Structures in Georeferenced Twitter Feeds

Recently we introduced the ohsome platform for OSM History Analytics. Now we want to give you a high-level overview of the ohsome platform and the components it consists of.

Ohsome API

The ohsome platform is based on a three-layers API architecture, where the flexibility, as well as the usage-complexity are increasing going from the top to the bottom.

To demonstrate how you can use the ohsome API, look at this example request:

https://api.ohsome.org/v0.9/elements/count/groupBy/boundary? bboxes=Kathmandu:85.2,27.6,85.45,27.8|Pokhara:83.9142,28.1693,84.0775,28.2687& types=way&time=2015-01-01/2017-01-01/P1M& keys=building&values=residential&showMetadata=true

The result of this request is the length of the selected elements for each month: all primary highways in the bounding box named ‘Kathmandu’ given by the coordinates of its corners. The time parameter chooses a time range (2015-01-01/2017-01-01) and requests to aggregate the result monthly (P1M).

The ohsome API is implemented using the OSHDB API, which is one level below of the ohsome API.


The OSHDB is a Java application that stores and serves the OSM history data. The OSHDB API is a functional interface that supports parallelisation of the computation based on the MapReduce programming model and, thus, may be run on a multi-instances cluster environment. The OSHDB API is more flexible but at the same time more complex than the ohsome API.

The following code-snippet shows the same example as above as a query to the OSHDB API.

  oshdb.areaOfInterest(new OSHDBBoundingBox(85.2,27.6,85.45,27.8))
    .timestamps("2015-01-01", "2017-01-01", OSHDBTimestamps.Interval.MONTHLY)
    .where("highway", "primary")
    .map((OSMEntitySnapshot t) -> Geo.lengthOf(t.getGeometry());
  SortedMap result = oshdb.sum();

OSHDB data layer
On the very bottom of the ohsome platform components stack is the OSHDB data layer. The OSHDB data layer is a specification of how the OSM data is stored, partitioned and indexed for the usage in our OSHDB. This digs deep into the core of our database and its data structures. It will leave you with a maximum of flexibility but also complexity. This is useful if you want to do crazy stuff or are a core programmer of the project.

Forthcoming posts will describe the OSHDB data layer/store format and usage in more detail. Stay tuned for updates and further examples of usage of the ohsome platform!

The production of Volunteered Geographic Information (VGI) is a type of human behavior which emerges via direct and indirect interactions with the physical environments described by these data. The nature of these interactions and the extent to which they rely on physical presence in the mapped area may affect the quality of the resulting digital representation, its completeness, and richness. Physical presence may also affect the formation of volunteer communities and their structures. For example, individual the contributions of individual M1 in the figure below, who is physically present in the area of interest, would probably reflect much local knowledge. This individual is also likely to develop place attachment which would encourage him/her to make repeated contributions. In comparison, by contributing from afar using an ancillary digital dataset, individual M2 may produce higher volumes of data. The richness of these however would rely on what information can be derived from the ancillary dataset. Hence, employing a space-time-behavior-based perspective for studying VGI may prove to be beneficial for understanding the characteristics of the data. In a recently accepted contribution to the VGI ALIVE workshop, time geographic concepts are reformulated for studying the production of VGI as a type of a physio-virtual behavior. The main one of these is the physio-virtual bundle – a space-time entity representing the conditions in physical and virtual spaces for a ‘data event’, i.e. a large-scale contribution over a short period of time. The nature of the bundle, derived from the characteristics of the relevant space-time domain, determines the nature of contributions. For example, changing accessibility conditions within the domain may determine the share of M1-like contributions in the data.
As a first step towards formalizing a broader analytical approach, these concepts are applied within the analysis of two case studies of OpenStreetMap (OSM) historical datasets – the city of Tel-Aviv Jaffa (TLV) in Israel and the Gaza Strip (GS). The first is characterized by high accessibility levels while in the other mobility in and out of the area is tightly regulated by military forces. Three different bundles were identified, one in TLV and two GS, with varying characteristics in terms of number of contributors, their location, and their motivation. The effects of these bundles on data quality are tightly related to conditions within the area and how these conditions shape community structures. For instance, more local knowledge (represented by number of tags) was included in the data when the area of interest was physically accessible; more regulation of the data was practiced when mapper communities presented a hierarchical structure with a core of highly involved mappers. The results thus show that bundles can have different characteristics and that these lead to different trends within the data, thus pointing towards possible formalizations of effects within the development of a broader analytical approach.

Hear the results and full story at VGI-ALIVE Workshop at AGILE 2018 (Lund, Sweden) and join the discussion there!

Grinberger, Y. (2018, accepted): Identifying the Effects of Mobility Domains on VGI: Towards an Analytical Approach. VGI-ALIVE Workshop at AGILE 2018. Lund, Sweden.

(Figure by Y. Grinberger 2018).

The organizers are happy to announce that list of accepted papers at the VGI ALIVE workshop are available. Therefore a prelimnary draft agenda of the workshop at AGILE 2018 in Lund has been posted. Looks like an interesting and promising programme!

Stay tuned for further updates and join us for discussions and collaborative sessions in Lund!

This is the preliminary timetable. Timings will change slightly as we finalise the progamme for VGI-Alive 2018. Please refer to the website for the official timings of sessions.

Tuesday 12th June 2018, Lund, Sweden at AGILE 2018

Time Program Item
08:45 - 09:00 Welcome and Introductions
09:00 - 09:45 Keynote (TBA) presentation and discussion
09:45 - 11:05

SESSION 1: Analysis of Contributors

  1. Analyzing the spatio the spatio-temporal dynamics of Snapchat
    AUTHORS Levente Juhász and Hartwig H. Hochmair Geomatics Program, Fort Lauderdale Research and Education Center, University of Florida, USA
  2. Identifying the Effects of Mobility Domains on VGI: Towards an Analytical Approach
    AUTHORS: A. Yair Grinberger, Heidelberg University, GIScience Research Group, Heidelberg, Germany
  3. The Impact of Biases in the Crowdsourced Trajectories on the Output of Data Mining Processes
    AUTHORS: Anahid Basiri, Muki Haklay, Zoe Gardener, University College London, UK
  4. Enhancing Crowdsourced Classification on Human Settlements Utilizing Logistic Regression Aggregation and Intrinsic Context Factors
    AUTHORS: Benjamin Herfort and Alexander Zipf, GIScience Research Group, Heidelberg University, Heidelberg, Germany
11:05 - 11:35 Tea/Coffee Break with other AGILE 2018 workshops
11:35 - 12:50

SESSION 2: VGI and Crowdsourcing Analysis of the built and natural environments.

  1. Air Trails – Urban Air Quality Campaign Exploration Patterns
    AUTHORS: Martin Becker, Florian Lautenschlager, Andreas Hotho, University of Würzburg, Würburg, Germany
  2. A discussion of crowdsourced geographic information initiatives and big Earth observation data architectures for land-use and land-cover monitoring
    AUTHORS Luiz Fernando F. G. de Assis, Karine Reis Ferreira, Lúbia Vinhas: Brazilian National Institute for Space Research (INPE), São José dos Campos, Brazil Téssio Novack, Alexander Zipf, GIScience Research Group, Heidelberg University, Heidelberg, Germany
  3. Crowd-sourced information on building façades - A comparative study on the use of commercial and non-commercial crowdsourcing platforms
    AUTHORS: Robert Hecht, Tim Wendt, Martin Behnisch, Leibniz Institute of Ecological Urban and Regional Development, Dresden, Germany
  4. Associating OpenStreetMap tags to CORINE land-cover classes using text and semantic similarity measures
    AUTHORS: Tessio Novack, Janek Voss,Michael Schultz, Alexander Zipf, GIScience Research Group, Heidelberg University, Germany
12:50 - 13:00 Recap and planning for the afternoon session
13:00 - 14:30 Lunch
14:30 - 15:30 Collaborative Session - breakout groups on selected VGI-Alive 2018 topics. Topics will be announced in advance of the workshop.
15:30 - 16:00 Tea/Coffee Break with other AGILE 2018 Workshops
16:00 - 16:30 Report back from Collaborative sessions
16:30 - 17:00 Wrap up, final remarks, Closing of Workshop.

With the aim of rapidly estimating the updated state of the CORINE land-cover map at the frequency with which the OpenStreetMap (OSM) dataset is edited and extended, we propose an approach for automatically associating widely used OSM tags to Level 1 and Level 2 CORINE land-cover classes. This association is probabilistic and is undertaken based on different text and semantic similarity methods. The input to these methods are the tag and class descriptions found on the OSM Wiki and CORINE Nomenclature documents, respectively. Thirty different associations were undertaken based on different text and semantic similarity methods. The methods performances were evaluated based on reference data produced by five land-use/land-cover experts. The best association accuracy achieved for Level 1 (5 classes) was of 90%. However, for Level 2 (15 classes), the best achieved accuracy was of only 62%. In this paper, we also present an approach for potentially improving the CORINE map derived from OSM by letting the tag-class association probabilities from OSM features to be influenced by the probabilities of their neighbouring OSM features. We explain how this may lead to more accurate and spatially smoothed CORINE land-cover maps derived from OSM.

Because close geographical entities tend to be similar or related, letting the context, i.e. the class probabilities of the features spatially near a given feature, influence the class probabilities of this given feature may lead to an overall better labelling of the whole scene. In our context, the probabilities are the tag-class associations (which are normalized), and the features are OSM features containing these tags. Formally speaking, we let the CORINE class probability distribution of an OSM feature to be dependent on the tag-class association probabilities of its nearby OSM features. These dependencies can be modelled as a Markov Random Field. This idea will be implemented in the upcoming weeks and discussed in the workshop.

Hear the results and full story at VGI-ALIVE Workshop at AGILE 2018 (Lund, Sweden) and join the discussion there!

Novack, T., J. Voss, M. Schultz, A. Zipf (2018, accepted): Associating OpenStreetMap tags to CORINE land-cover classes using text and semantic similarity measures. VGI-ALIVE Workshop at AGILE 2018. Lund, Sweden.


See also: OSMlanduse.org Our WebService providing LULC information derived from OpenStreetMap. (global)

Related earlier work:

Schultz, M., Voss, J., Auer, M., Carter, S., and Zipf, A. (2017): Open land cover from OpenStreetMap and remote sensing. International Journal of Applied Earth Observation and Geoinformation, 63, pp. 206-213. DOI: 10.1016/j.jag.2017.07.014.

Jokar Arsanjani, J., Mooney, P., Zipf, A., Schauss, A., (2015): Quality assessment of the contributed land use information from OpenStreetMap versus authoritative datasets. In: Jokar Arsanjani, J., Zipf, A., Mooney, P., Helbich, M., OpenStreetMap in GIScience: experiences, research, applications. ISBN:978-3-319-14279-1, PP. 37-58, Springer.

Jokar Arsanjani, J., Helbich, M., Bakillah, M., Hagenauer, J., & Zipf, A. (2013). Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science, 2264-2278. DOI:10.1080/13658816.2013.800871.

Among semi-automated methods and pre-processed data products, crowdsourcing is another tool which can help to collect information on human settlements and complement existing data, yet it’s accuracy is debated. Whereas the potential of crowdsourced datasets for training of machine learning algorithms has been explored recently, only few work has been done towards utilizing machine learning techniques to enhance the crowdsourcing workflow itself. In recent research we investigated a novel approach that incorporates logistic regression to aggregate crowdsourced classification on human settlements from the MapSwipe app. For a case study containing 941,589 mapping tasks, we analysed to what degree such an approach can improve data quality utilizing intrinsic context factors such as user agreement, user characteristics and spatial characteristics of the results.
The results have shown that a logistic regression based aggregation of crowdsourced classifications produced significantly higher quality data than common approaches that use soft majority agreement. The findings pronounce that the integration of machine learning techniques into existing crowdsourcing workflows can become a key point for the future development of crowdsourcing applications. However, regarding the limited geographic scope of this research, further validation of the automated classification and its transferability need to be addressed in future investigations.
Intelligent crowdsourcing approaches can dynamically derive data quality indicators to improve the task allocation process. For instance, for tasks reaching a high credibility no further classification should be obtained, whereas uncertain tasks should be repeated or validation should be prioritized. This could reduce the amount of required crowdsourced classifications while maintaining high quality. The setting bears great potential for features where fully automated techniques still fail to produce reasonable data quality.

Herfort, B., Zipf, A. (2018 accepted): Enhancing Crowdsourced Classification on Human Settlements Utilizing Logistic Regression Aggregation and Intrinsic Context Factors. VGI-ALIVE Workshop. at AGILE 2018. Lund, Sweden.

MapSwipe Analytics: http://mapswipe.heigit.org/ by HeiGIT

MapSwipe Analytics by HeiGIT

Accessibility is a widely discussed topic and there are a growing number of efforts to sensitise European cities and municipalities to the topic. A focus of such efforts is the facilitation of easy access to public places for everyone, including those with walking disabilities, balance and visual disorders, as well as people requiring the use of walking aids, and parents with pushchairs. To date, finding suitable ways through the city is challenging for people whose walking ability is permanently or temporarily limited. This problem affects millions of people in Europe. As the citizens and visitors of Heidelberg also belong to this growing group of people, the City of Heidelberg, HeiGIT/ GIScience Research Group and MatchriderGO are currently working on a project to increase mobility for people whose walking ability is permanently or temporarily limited.

For people with limited walking ability it is (among other things) important to know about streets with sidewalks that are of a certain width, the presence of small inclines, and whether the surface is of a suitable material. To enable crossing at road junctions, dropped kerb information should also be available. The current project “Routing for Accessibility” will enable people with limited mobility to better plan their route through the city using their smartphones and thereby enable them increasing social participation despite their physical constraints.

The smartphone app will facilitate routing based on Openrouteservice (ORS) using OpenStreetMap (OSM) data between a defined start and end point. But in particular specific options and values defined by the users will be taken into account as well, e.g., regarding maximum inclines, sidewalk width and kerb heights. The project will focus on the city center, including 40 kilometers of roads. The focus area includes the world famous old town that is visited by 12 million tourists per year as well as the central railway station.

The project builds on and extends work and insights of the EU funded CAP4Access project of Heidelberg University and other European partners.

Selected earlier Work:

Hahmann, S., Miksch, J., Resch, B., Lauer, J., Zipf, A. (2017): Routing Through Open Spaces - A Performance Comparison Of Algorithms. Geo-Spatial information Science, 2017. Taylor & Francis. Geo-Spatial information Science, 2017. Taylor & Francis. https://doi.org/10.1080/10095020.2017.1399675

Zipf, A., Mobasheri, A., Rousell, A. ,Hahmann, S. (2016): Crowdsourcing for individual needs - the case of routing and navigation for mobility-impaired persons. In: Capineri, C, Haklay, M, Huang, H, Antoniou, V, Kettunen, J, Ostermann, F and Purves, R. (eds.) European Handbook of Crowdsourced Geographic Information, p. 325–337. London: Ubiquity Press. DOI: dx.doi.org/10.5334/bax.x

Mobasheri A., Huang H., Degrossi L.C. and A. Zipf (2018): Enrichment of OpenStreetMap Data Completeness with Sidewalk Geometries Using Data Mining Techniques. Sensors 2018, 18(2), 509; doi:10.3390/s18020509

Rousell A. and Zipf A. (2017): Towards a landmark based pedestrian navigation service using OSM data. International Journal of Geo-Information, ISPRS IJGI, 6(3): 64.

John, S., Hahmann, S., Rousell, A., Loewner, M., Zipf, A. (2016). Deriving incline values for street networks from voluntarily collected GPS traces. Cartography and Geographic Information Science (CaGIS). Taylor & Francis. DOI:10.1080/15230406.2016.1190300

Fan, H., Yang, B., Zipf, A. , Rousell, A. (2015): A polygon-based approach for matching OpenStreetMap road networks with authority data. International Journal of Geographical Information Science (IJGIS), volume and issue pending, pp. pending, Taylor & Francis. DOI:10.1080/13658816.2015.1100732

Neis, A. & Zielstra, D. (2014): Generation of a tailored routing network for disabled people based on collaboratively collected geodata. Applied Geography. Vol. 47, pp. 70–77.

Müller, A., Neis, P. & Zipf, A. (2010): Ein Routenplaner für Rollstuhlfahrer auf der Basis von OpenStreetMap-Daten. Konzeption, Realisierung und Perspektiven. AGIT 2010. Symposium für Angewandte Geoinformatik. Salzburg. Austria.

wie in jedem Semester findet auch im Sommersemester 2018 das Geoinformatik-Kolloquium am Geographischen Institut statt.

Hierzu möchten wir Sie herzlich einladen! Internationale Gäste berichten aus ihren interessanten Forschungsprojekten. Nutzen Sie die Gelegenheit, sich über die neuesten Entwicklungen in der Geoinformatik und in angrenzenden Forschungsfeldern zu informieren und mit Forschern aus aller Welt zu diskutieren!

Das Geoinformatik-Kolloquium findet zu ausgewählten Terminen in der Regel montags um 14:15 h in INF 348, Raum 015 statt. Alle Vorträge sind in englischer Sprache.

Bisher feststehende Termine im Sommersemester 2018:

Mon. 23.04.2018 Mathias Gröbe / Technical University of Dresden, Department of Geosciences, Institute of Cartography:
Micro Diagrams: A Multi-Scale Approach for Geovisual Analysis of Categorised Point Datasets

Mon. 30.04.2018 Evelyn Schmitz /  FARO Europe:
Voxel-based change analysis of hypertemporal terrestrial laser scanning point clouds of the research campus ARENA2036 including factory interior for the development of a digital twin

Mon. 07.05.2018 Prof. Dr. Begum Demir / Electrical Engineering and Computer Science, Technical University of Berlin:
Recent Advances in Remote Sensing Image Search and Retrieval from Large Archives

Mon. 28.05.2018, Dr. Jack William / Department of Geography, Durham University UK:
Near-continuous monitoring of rockfalls and insights into post-seismic landslide patterns

Mon. 11.06.2018 Prof. Bisheng Yang / LIESMARS at Wuhan University:
A low-cost mini UAV Laser Scanning System - Kylin Cloud: Design and Performance

Mon. 18.06.2018 Lukas Winiwarter / TU Wien, Department of Geodesy and Geoinformation:
Classification of 3D Point Clouds using Deep Neural Networks

Mon. 09.07.2018 Prof. Jiangya Gong / LIESMARS at Wuhan University:
Big spatiotemporal data analysis based on social sensing

Weitere Informationen finden Sie hier: http://www.geog.uni-heidelberg.de/gis/talks18.html Wir freuen uns auf Ihr Kommen!

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