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Last week we have been upgrading the openrouteservice, and with that has come the ability to include elevation information in routing pretty much anywhere on the globe (sorry people on Antarctica, but we don’t have routing for you just yet). So if you want to know the elevation and steepness of your drive from Svalbard airport to the EISCAT Svalbard Radar Station, now you can!

Probably more useful however is that the elevation information is now available for more “accessible” locations such as the north of Norway, and the whole of Sweden. Other places such as Iceland also now have elevation available which could be of great benefit if, for example, you are planning to travel around the country on a bike and want to know where you will be hitting the tough spots.

Before this release, openrouteservice made use of SRTM elevation data as its sole source of elevation which restricted us to providing elevation and steepness information only up to 60 degrees north of the equator. With an update to the service which now makes use of a newer version of Graphhopper, GMTED2010 elevation data can also be used which provides us with the information needed for locations found farther north.

There will be more features coming along with this update, so stay tuned for more information about those!

After the participants of the practical field training had arrived in Fès yesterday and spent the evening there, the group is now on the way to the Erg Chebbi east of the Anti-Atlas mountains. Thereby, the group passes various gemorphologic landscape units including the Western Meseta, Middle Atlas and High Atlas mountains. Moreover, we crossed the watersehd between the catchment area of Moulouya river draining to the Mediterranean Sea and Souss river draining into the Atlantic Ocean.

Tomorrow, data acquisition will start in the Erg. We will keep you updated with further posts.

You might also be interested in reading the blog post from Thursday.

The social functionality of places (e.g. school, restaurant) partly determines human behaviors and reflects a region’s functional configuration. Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces. Assuming their potential impacts on human verbalization behaviors, one possibility is to link the functions of places to verbal representations such as users’ postings in location-based social networks (LBSNs). In a recently published study, we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places. We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places, which enables a reliable verification of the semantic concepts identified from user-generated text snippets. A latent semantic analysis is conducted on a large Foursquare check-in dataset. The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization. To further elaborate the representativeness of text messages, this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts (i.e., representativeness), and another investigation on semantic concepts to quantify how well they can be represented by text messages (i.e., representability). The results shed light on featured terms with strong locational characteristics, as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations.

Furthermore, we found that some terms are strongly associated with (and representative for) certain semantic concepts. In this study, we proposed an entropy-based approach to quantify the representativeness of terms (RQ 2), and successfully identified representative terms such as justkeepswimming (Pool) and bowlathon(Bowling Alley), and un-representative terms such as just, really, time, and lol that may appear ubiquitously at any location.

Finally, under the assumption that some semantic concepts may have heavier impacts on users’ verbalizations and can thus be better represented by textual snippets due to linguistic uniqueness, we proposed an approach based on cosine similarity to quantify the representability of semantic concepts (RQ 3). The representability scores are verified with a prediction experiment, and results show that the prediction precision is highly correlated with the representability score assigned by our approach.

In general, our study presents comprehensive investigations on the possibility of obtaining semantic knowledge about geographic locations using text messages. The findings on the representativeness of terms and representability of semantic concepts can be further used to improve the LSA model or other text mining approaches by, e.g. tuning the weighting schema.

It should be pointed out that the way how we quantify the representativeness is scale-dependent. For example, it has been mentioned that the term dinner is representative for a generic restaurant, but not for a specific type of restaurant. It can be expected that when the semantic concepts are described at a coarser conceptual scale (e.g. without distinguishing the exact restaurant types), the same term would exhibit much higher representativeness.

With the Foursquare dataset, a LSA model has been constructed with reliable prior knowledge. Theoretically, this model can be used to detect latent semantic concepts of places from text messages of other sources such as Twitter tweets, and the feasibility has already been demonstrated with our prediction experiment. However, users may use different LBSN platforms for different reasons under different scenarios, and this may affect the performance of the identified model for cross-dataset usage. It would still be interesting to apply this model onto datasets from other platforms, because the results of such comparison might reveal some variations in platforms with respect to the usage patterns.

More details can be found in:

Pages: 159-172

Here you can find also the further contributions of the GSIS Special Issue: Crowdsourcing for Urban Geoinformatics. Geo-Spatial Information Science (GSIS), Volume 23, Issue 3. Taylor & Francis.

At the recent EuroGEOSS Workshop in Geneva, LandSense researchers from IIASA and Heidelberg University hosted an interactive mapping session to showcase the power of crowdsourcing for map validations as announced earlier.

Using the openly available Land Cover Validation Platform (LACO-Wiki), participants collaboratively validated a land use and land cover map of Geneva, which brings together data from both earth observation (ESA’s Sentinel 2) and OpenStreetMap data streams (see soon more at OSMlanduse.org, read also here).

During this session participants gained first-hand knowledge on how microtasks distributed to a crowd can be an efficient approach to validate maps using high resolution satellite imagery. Furthermore, degree of agreement between the contributors was presented as a means of addressing the quality and confidence of the crowd-driven approach. The session was very successful with validations of some 750 points within just 15 minutes!

Participants including members of the academic community, industry and representatives from the European Commission’s Executive Agency for SMEs. LandSense partners will launch a series of such interactive mapping sessions in the upcoming months to contribute to the world’s first crowdsourced global land use map.

The event is a prequel to our upcoming release of a conterminous/gap-free Sentinel-2 and OSM based land use map for all EU member states, as announced recently. The final map will be featured on osmlanduse.org, stay tuned as we are very exited for the upcoming map release.

Selected related 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

See here for further related references.

As part of the practical field training and the small research group “Erg Chebbi”, 16 students will explore physical geography and 3D GIScience in an impressive aeolian sand dune environment in Morocco from 5 October to 15 October. The Erg Chebbi is one of the smallest Ergs in the Sahara and is situated within the large sedimentary Tafilalt basin in southeast Morocco.

The field training is also embedded in a PhD project which investigates the dynamic evolution of aeolian star dunes in the Erg Chebbi using a multi-sensor approach: With the help of terrestrial LiDAR, remote sensing, sediment analysis, electrical resistivity tomography (ERT) and ground penetrating radar (GPR), the students will capture multi-source datasets for the analysis of surface and subsurface characteristics of star dunes.

Prof. Olaf Bubenzer, Prof. Bernhard Höfle, Dipl.-Geogr. Manuel Herzog and Katharina Anders M.Sc. will supervise the field training and, thus, combine physical geography and 3D geospatial data processing.

Part of the group together with the measurement equipment have already arrived in Morocco by car and ship and are now waiting for the rest of the group to arrive on Friday.

We will keep you updated about the field trip with further posts - stay tuned!

In the case of a disaster fast response is important and life saving. Information on blocked streets is crucial, but a the same time this infornations needs to be considered by routing engines in real-time. However, many routing engines use street network data which is at best updated once a week or even less often. For many proprietary routing engines which do not use OpenStreetMap data it is also hardly possible to figure out when the data was updated for the last time.

To tackle this issue, we created a new jupyter notebook which shows how the direction feature avoid_polygons of the OpenRouteService API can be used for this purpose. It allows to avoid certain areas (e.g. flood affected regions) and to request the fastest or shortest route for different kind of travel profiles (e.g. car, pedestrian or truck). We used Twitter data which offers real time location based point data which can be used as a fast ground truth information.

We apply our method to the 2013 River Elbe Flood event. This severe flood affected many cities in the eastern part of Germany, and also the city of Magdeburg, which is our study region

For further reading regarding the use of social media data in disaster situations take a look at our related publication.

Porto de Albuquerque, J., B. Herfort, A. Brenning, A. Zipf (2015): A Geographic Approach for Combining Social Media and Authoritative Data towards Improving Information Extraction for Disaster Management: A Study on the Twitter usage in the River Elbe Flood of June 2013. International Journal of Geographical Information Science, 29(4): 667-689. Taylor & Francis. DOI: 10.1080/13658816.2014.996567

Airborne laser scanning (ALS) data of the Arctic permafrost research region Trail Valley Creek (TVC) has just been published for open access on the data library PANGAEA:

Anders, Katharina; Antonova, Sofia; Boike, Julia; Gehrmann, Martin; Hartmann, Jörg; Helm, Veit; Höfle, Bernhard; Marsh, Philip; Marx, Sabrina; Sachs, Torsten (2018): Airborne Laser Scanning (ALS) Point Clouds of Trail Valley Creek, NWT, Canada (2016). PANGAEA, DOI: 10.1594/PANGAEA.894884

The data was acquired in 2016 using the AWI Polar5 science plane and subsequently processed in the frame of the PermaSAR project in a joint effort of the 3DGeo research group and the Permafrost Research Unit at AWI. The classified point cloud is published together with the derived Digital Terrain Model (DTM) and vegetation height rasters. The terrain probability method used to classify ground points in the ALS data in the frame of the PermaSAR project is available on the GIScience gitlab as Python script.

Extract of ALS data colored by amplitude values

Screenshot: Extract of ALS data colored by amplitude values

By the way, the acquisition was repeated this year, so that multitemporal analyses will be possible as soon as the 3D point cloud data of 2018 is processed.

The PermaSAR project is funded by the BMWi/DLR in the framework “Entwicklung von innovativen wissenschaftlichen Methoden und Produkten im Rahmen der TanDEM-X Science Phase”.

Informationen und Navigation zu urbanen Grünflächen in Städten – meinGrün

Neues mFund Kooperations-Projekt meinGrün wird vom BMVI gefoerdert.


Um in Städten trotz Wachstum und Nachverdichtung eine hohe Lebensqualität zu sichern, spielen Grünflächen eine essentielle Rolle, da sie sich positiv auf Stadtklima und Biodiversität auswirken und als Orte der Naturerfahrung und Entspannung dienen. Bürgerinnen und Bürger sollten daher wissen, wo Grünflächen liegen, welche Ausstattungsmerkmale diese haben und wie diese am besten zu Fuß oder per Rad erreicht werden können.


Ziel des Vorhabens ist die experimentelle Entwicklung und Erprobung einer neuartigen Infrastruktur von Diensten und der App „meinGrün“, die zu verbesserten Informationen zu Grünflächen in Städten führt. Dies soll Anreize schaffen, die Alltagsmobilität beim Aufsuchen städtischer Grünflächen nachhaltiger und gesünder zu gestalten. Die App stellt vorhandene Informationen dar und erlaubt eine nutzerspezifische Bewertung der Grünflächen sowie ein Routing unter Berücksichtigung neuartiger Umgebungsparameter (z. B. Verschattung, Grünheit, Lärm).


Durch Kombination vorhandener offener Geodaten mit neuesten Fernerkundungsdaten der Sentinel-Mission und nutzergenerierten Daten (OpenStreetMap, Social-Media) werden Informationen zur physischen Struktur, Ausstattung sowie Nutzung und Wahrnehmung von Grünflächen abgeleitet. Diese Informationen dienen als Eingangsdaten für ein Entscheidungsunterstützungssystem zur Auswahl von Grünflächen und Wegen zu diesen. Anhand der Pilotstädte Dresden und Heidelberg wird der Verbund aus Dienstinfrastruktur und der App „meinGrün“ entwickelt und getestet.

Vorarbeiten GIScienceHD/HeiGIT:
Experimentelle Prototyp-Studien (für Deutschland) in OpenRouteService zu gesunden Fussgängerrouten, insb. unter Berücksichtigung von

- Grünflächen

- Lärmbelastung

- Soziale Attraktiviät (vibrancy)

Verbundkoordinator Leibniz-Institut für ökologische Raumentwicklung e.V. (IÖR), Dresden

  • Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR), Oberpfaffenhofen
  • Heidelberg Institute for Geoinformation Technology (HeiGIT), GIScience Research Group, Universität Heidelberg
  • Institut für Kartographie (IFK), Technische Universität Dresden
  • ISB Institut für Software-Entwicklung und EDV-Beratung AG, Karlsruhe
  • urbanista GmbH & Co. KG, Hamburg
  • Terra Concordia gUG, Berlin
ORS Shortest Pedestrian Route (Mannheim)

ORS Shortest Pedestrian Route (Mannheim)

ORS GREEN Pedestrian Route (Mannheim)

ORS GREEN Pedestrian Route (Mannheim)

This week, the GIScience research group received a visit by Prof. Mohammad Reza Malek and his Students from K.N.Toosi University of Technology, Teheran, Iran, supported by DAAD. In three intense days of  talks and discussions, Prof. Malek and his students presented their work at Ubiquitous & Mobile GIS Research Lab led by Prof. Malek in the fields of Volunteered Geographic Information (VGI), Location-based Services and Location-based Social Networks. Talks by our guests included topics like mathematical frameworks for line graph analysis, point-free topology approaches, combining crowdsourcing of geodata and sensor networks for alerting systems, and applications of VGI in diverse fields, like acquiring 3D data of cities,  incident information in electricity networks, visual pollution, and incidents concerning water resources and utilities. Other talks presented work on research in VGI quality and credibility, context-aware spatial services, augmented reality for tourism, seamless outdoor and indoor navigation, and many many more. Colleagues from GIScience research group at Heidelberg University used the opportunity to present their current research and gain new insights from discussions with our guests. Not least, the visit presented a great opportunity for all participants to widen our cultural perspective and to share experience.

If you missed the live stream, you can simply watch the lecture “Big Point Clouds == Deep Information? Current Progress in Geospatial 3D/4D Point Cloud Analysis” on the CiTiUS Youtube Channel, University of Santiago de Compostela, here: https://www.youtube.com/watch?v=5Wb4ozyTGVg

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