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This week 19.-23.10. the autumn school Urban Data Science takes place as a online course set up together by GIScience Heidelberg and the Institute for Transport Studies (IfV), KIT. It is part of an ongoing application for a HeiKA (Heidelberg Karlsruhe Strategic Partnership) project that would foster joint teaching modules between GIScience HD and IfV KIT.
The autumn school is lead by Tamer Soylu (KIT), Tessio Novack (GIScience) and Sven Lautenbach (HeiGIT). In addition to talks by experts from business and research the autumn school consists of practical labs on data science in the domain of urban transportation. Prof. Vortisch (KIT) and Prof. Zipf (HD) introduce their respective research fields.
Participants consist of Master students of Geography and Mobility and Infrastructure from Heidelberg and KIT.

The new openrouteservice map client development is in the final phase and has now a new feature that allows to show routes, isochrones and other features from openrouteservice (ORS) integrated on other websites. This allows webmasters and editors to embed those geographic features into their website using the new client.

How to embed the maps

Any feature visualized on the new map client, like a route, isochrones or a place can be used as an embedded content.
The code to be used to embed it is automatically generated on the map client itself. To have access to the code on the sidebar just click on the share option and the iframe code will be available. You just need to click on the code and it will be copied to your clipboard.

Fig. 1: Access to the embedding code via share

You can find some examples of embedded map views online at:

(navigate to openrouteservice.org -> Jupyter Examples -> Embedded Mode for openrouteservice VueJS client )

Some features of the embedding mode

– Place information: when clicking on the map, the user can investigate the underlying location. The place information is shown in a popup on the bottom right.

– Measuring distances: by using the measuring control on the left side of the view, the user is able to measure distances on the map in different units. The distances can be discarded by clicking on the control a second time.

– Switching basemap: to view a different map in the background, the user can choose from six different basemaps by hovering over the layer switcher in the top right of the map view.

– Accessibility mode: just like the new VueJs client, the iframe comes with an accessibility mode to navigate the view with the keyboard (arrow keys, tab …) instead of the mouse.

– Zoom to all features: last but not least, there is a button to easily zoom to the full extent of all features (after zooming in/out) on the right hand side of the map view.

- Mobile friendly: like the new map client itself, the embedded map view is also mobile friendly. By default, the iframe code generated used 100% for width and height, so that it can adapt itself to the available dimension.

- User gestures handling: to avoid the map view to be scrolled when the user has the intention to scroll the entire page we added a gesture handling in the embedded mode in order to keep the user in control of it. If the user wants to scroll the map he has to user CTRL + scroll (desktops) or two fingers (mobile) to scroll the map view content.  When the user scrolls the page a message with instructions to scroll the map is displayed over the map view.

Focused on visualization: most of the interactivity does not work, but the user can click on the  view on ORS button to go to the full version of the client and interact with it.

Language selection: although only English is supported now on the map client application, soon more languages will be available. When the iframe code is generated, the current defined language is also part of the code, so that when new languages are available the displaying language of the embedded map view will be manageable.


  • ORS Ref:
    Neis, P. & Zipf, A (2008): OpenRouteService.org is three times “Open”: Combining OpenSource, OpenLS and OpenStreetMaps. GIS Research UK (GISRUK 08). Manchester.
On October 16-18 there is the Copernicus Hackathon “Barcelona” -
Integrating Copernicus services and state-of-the-art tools within Weather-induced emergency management
HeiGIT is involved in the Copernicus Hackathon by providing free and open web-based services and APIs which leverage OpenStreetMap data as valuable tools for disaster management.
The HeiGIT services include: OpenRouteService API, sources, and clients, and the ohsome OpenStreetMap History Analytics framework (ohsome API, ohsomeHex, ohsome2X and the OSHDB).
The event has moved online due to Corona. You can participate by registering here!

About this Event

Copernicus Hackathon Barcelona is open for developers, designers, data wranglers, data journalists, data enthusiasts and everyone interested in exploiting potential of cutting edge developments in weather forecasting, risk modelling and earth observation in improved information and services for emergency management and population to protect their lives.

Participants are requested to use the provided tools and datasets in order deliver software developments supposing added value downstream services, such as:

  • Develop web applications for policy- and decision-makers.
  • Apps & services for specific public and commercial needs (e.g. tourism, energy, transport).
  • Effective communication of risks (environmental or societal challenges) to the public (participatory approach, crowdsourcing and social media data).

Copernicus services provided:

  • Emergency Management,
  • Climate Change,
  • Atmosphere


The best teams will be awarded the following prices at the end of the Hackathon:

  • Cash prize (2000 euro) for the winning team.
  • Cash prize (1250 euro) for the 2nd best qualified team.
  • Cash prize (750 euro) for the 3rd best qualified team.
Further Information: https://barcelonahack.com/

The E-TRAINEE project is a new collaboration project for developing an “E-learning course on Time Series Analysis in Remote Sensing for Understanding Human-Environment Interactions” with Markéta Potůčková (Department of Applied Geoinformatics and Cartography, Charles University Prague) as PI of the project and Heidelberg University, University of Innsbruck and University of Warsaw as project partners. The E-TRAINEE project is funded in the framework of the Erasmus+ programme of the European Union.

The project’s objective is to develop a comprehensive research-oriented open e-learning course on time series analysis in remote sensing for environmental monitoring. The course offers a multidisciplinary approach connecting themes from computer science, geography, and environmental studies.

It combines well-established and latest technologies of remote sensing (satellite and UAV sensing, multispectral and hyperspectral sensing, 3D point clouds) and methods of artificial intelligence (machine and deep learning) in order to use these technological developments to understand environmental changes and interaction of human activities and environment. It shows how the same environmental phenomenon can be analysed from the perspective of different data sources, scales and time frequencies.

The 3DGeo research group supports this project with contents on 3D/4D geospatial point clouds and methods for their analysis, including machine learning, time series analysis, and laser scanning simulation. Contents will further comprise programming for point cloud analysis in Python and research-oriented case studies.

We are looking forward to bring our 4D research into international education!

The collaboration project follows the alliance built up through the 4EU+ collaboration project “3D Landcover Monitoring”.

CALL FOR PAPERS –  18th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2021)

May 23-26, 2021, Virginia, USA – https://www.drrm.fralinlifesci.vt.edu/iscram2021/ Virginia Tech

Track: Geospatial Technologies and Geographic Information Science for Crisis Management (GIS)

Deadline for paper submissions: December 6, 2020

* Track Description

With crisis and hazardous events being an “inherently spatial” problem, geospatial information and technologies have been widely employed for supporting disaster and crisis management. This was further highlighted during the response to the 2020 Coronavirus pandemic, which is relying extensively on spatial analysis for managing  the virus dissemination pathways and fighting against the virus propagation. Therefore, geospatial methods and tools – such as Spatial Decision Support Systems (SDSS), Geographic Information Systems (GIS) architectures, Volunteered Geographic Information (VGI), spatial databases, spatial-temporal methods, as well as geovisual analytics technologies –  have a great potential to contribute to, understand the geospatial characteristics of a crisis, estimate damaged areas, define evacuation routes, and plan resource distribution. Collaborative platforms like OpenStreetMap (OSM) have also been employed to support disaster management (e.g., in near real-time mapping). Nevertheless, all these geospatial big data pose new challenges for not only geospatial data visualization, but also data modeling and analysis; existing technologies, methodologies, and approaches now have to deal with data shared in various formats, different velocities, and uncertainties. Furthermore, new issues have been also emerging in urban computing and smart cities for making communities more resilient against disasters. In line with this year’s conference theme, the GIS Track particularly welcomes submissions addressing aspects of geospatial information in disaster risk and crisis research, and how this geospatial information should embrace the interdisciplinary nature of crisis situations. This includes exploring bridges between geospatial data science methods and tools and other related fields, including (but not limited to): computing disciplines (e.g. AI and machine learning), social sciences (e.g.  socio-spatial aspects of risk and resilience, community resilience, participation and governance) and humanities (e.g. spatial humanities and spatial digital arts). We seek conceptual, theoretical, technological, methodological, empirical contributions, as well as research papers employing different methodologies, e.g., design-oriented research, case studies, and action research. Solid student contributions are welcome.

Track topics are therefore focused on, but not limited to the following list:

– Geospatial data analytics for crisis management
– Location-based services and technologies  for crisis management
– Geospatial ontology for crisis management
– Geospatial big data in the context of disaster and crisis management
– Geospatial linked data for crisis management
– Spatially explicit machine learning and Artificial Intelligence for crisis management
– Urban computing and geospatial aspects of smart cities for crisis management
– Spatial Decision Support Systems for crisis management
– Individual-centric geospatial information
– Remote sensing for crisis management
– Geospatial intelligence for crisis management
– Spatial data management for crisis management
– Spatial data infrastructure for crisis management
– Geovisual analytics for crisis management
– Spatial-temporal modeling in disaster and crisis context
– Crisis mapping and geovisualization
– Collaborative disaster mapping, citizen participation
– Public policies and governance for geospatial information
– Case studies of geospatial analysis/tools during a pandemic situation
– Empirical case studies

* Important Dates

Full research and insight papers:
– Submission deadline: December 6, 2020
– Decision notification: January 17, 2021

Short (WiPe) papers and Practitioner papers:
– Submission deadline: January 31, 2021
– Decision notification: February 28, 2021

* Paper submission guidelines



- Submission deadline for CoRe papers: December 6, 2020

- Notification of decision for CoRe papers: January 17, 2021

- Submission deadline for WiP and Practitioner papers: January 31, 2021

- Notification of decision for WiP and Practitioner papers: February 28, 2021


- Professor João Porto de Albuquerque*, j.porto@warwick.ac.uk University of Warwick
- Alexander Zipf zipf@uni-heidelberg.de University of Heidelberg
- Flávio Horita flavio.horita@ufabc.edu.br Federal University of ABC
- Michael A. Erskine michael.erskine@mtsu.edu Middle Tennessee State University

This Thursday, team members from HeiGIT will give a presentation at MSF’s Annual GIS Week. This is an internal event at MSF ( Médecins Sans Frontières (MSF) International, Doctors without Borders), which brings together all employees working on GIS and geographic information management related topics.

Our “OSM Data Dive” session will provide an introduction to OpenStreetMap based analysis to improve MSF’s field missions and better preparatory planning. Central to these analysis will be the tools developed at HeiGIT, e.g. openrouteservice, ohsomeApi, ohsomeHex or the OSHDB.

Find our more about our work related to Humanitarian Aid and Sustainable Development at our website.

We launched a validation campaign of our new 10meter resolution OSMlanduse product for the member states of the European Union. Please contribute to the validation here. A technique where contributions are checked against each other is implemented to promote quality of information. The mapathon comes in four themes: nature, urban, agriculture or expert.
While the expert campaign may be addressed exclusively by application professionals the themes nature, urban, agriculture can be done by anyone that is enthusiastic about geography. Contribute here and choose your flavor.

We described our new landuse map based on the combination of OpenStreetMap and Sentinel satellite data through machine learning in an earlier post. The validation effort is also featured during the EU regions week where a web presentation and an interactive workshop is conducted by Michael Schultz and Ana-Maria Raimond Tue 14, October 2020, 9:30 Click here to join the validation. The EU regions event is upon registration only and participation of the validation is open for everyone.

Urban campaign land use classes for validation

Urban campaign land use classes for validation

Agriculture campaign land use classes for validation

Agriculture campaign land use classes for validation

Nature campaign land use classes for validation

Nature campaign land use classes for validation

Interface of validation

Interface of validation

The new OSMlanduse map is developed, deployed and hosted also with support from HeiGIT (Heidelberg Institute for Geoinformation Technology).

Related Work:

It’s CITY CYCLING time – some of you may even be involved in your municipality - a good opportunity to have a look on the OpenStreetMap (OSM) cycling ways in our city Heidelberg.

Welcome to part 8 of our how to become ohsome blog post series. This time we will show you how to set up a more complex filter with several OR and AND combinations for the ohsome API to get the length of the mapped cycling ways in OSM. Like in part 4 of our series, we will again show you in a Jupyter Notebook how you can use Python to make this nice complex ohsome query and visualization in one go.

The idea is to analyse the mapped cycle ways in Heidelberg in OSM. Therefore we need to have a look on how cycling infrastructure is mapped in OSM. To set up the filter, we want to know which tags do we need to extract all the cycle lanes, ways and roads. There is more than one way to tag cycle ways, lanes or paths in OSM, described for example on this OSM wiki page. Instead of requesting every possible tag by itself, all combinations of tags that can be used to define a cycle way within OSM can be requested at once using our new filter parameter. This also prevents ways being counted twice, which might have several of these tags associated with them.

Our tag combination is based on Hochmair, Zielstra, and Neis’s paper Assessing the completeness of bicycle trails and designated lane features in OpenStreetMap for the United States and Europe. In their study they explored the cycling features in the United States and Europe. We take their filter combination and extend it with tags of the German cycling infrastructure mapping methods listed on the corresponding OSM wiki page. After a pre-query for each of the tag combinations we found out that for some of them no data was available for the region of Heidelberg, so we excluded them. As a result we got a filter that consists of 25 different tag combinations.

The final filter looks like following:

type:way and (
(bicycle=use_sidepath) or
(cycleway=opposite and oneway:bicycle=no) or
(sidewalk:right:bicycle=yes) or
(cycleway:right=shared_lane) or
(cycleway:left=track) or
(cycleway:right=track) or
(highway=track and bicycle=designated and motor_vehicle=no) or
(highway=path and bicycle=yes) or
(highway=path and (bicycle=designated or bicycle=official)) or
(highway=service and (bicycle=designated or motor_vehicle=no)) or
(highway=pedestrian and (bicycle=yes or bicycle=official)) or
(highway=footway and (bicycle=yes or bicycle=official)) or
(highway=cycleway) or
(cycleway in (lane, opposite_lane, shared_busway, track, opposite_track)) or
(cycleway:left in (lane, shared_busway)) or
(cycleway:right in (lane, shared_busway)) or
(cycleway:both=lane) or
(bicycle_road=yes and (motor_vehicle=no or  bicycle=designated)) or
For the city of Heidelberg we get a cycleway length of about 167 km of mapped cycle infrastructure in OSM. Here you see the evolution of the length of the mapped cycle ways in Heidelberg from end of 2008 until middle of 2020:

The official number given by the city of Heidelberg of cycle path network is about 480 kilometers, which is almost 3 times as many kilometers as there are in OSM. The difference may be due to the fact that there are some side roads that have an extra lane, others do not, or that sometimes a appropriate tag is really missing in OSM. In addition, in the explanation of the cycle road map for Heidelberg, the city’s network includes normal roads which have signposted cycle routes running through to neighbouring communities such as Leimen, Eppelheim, Dossenheim and Edingen.

We can also take a spatial look at the current expansion of the cycle path network. For this we use the same filter as above but in the data extraction endpoint of the ohsome API. A snippet of the request can be found here.

The following map shows an extract of that data as it was by the end of June 2020 displayed on parts of the city of Heidelberg.

So if you are interested in the mapped cycling infrastructure in OSM in your city, just change the bounding box geojson in the code and find it out (Lächeln).  The complete Jupyter Notebook with all the code and explanations can be found here.

If you want to know more about our ohsome framework, don’t hesitate to reach out to us via info(at)heigit.org or contact any member of our team directly. Stay ohsome and happy cycling!

Information on the ohsome OpenStreetMap History Data Analytics Platform and more examples of how to use the ohsome API can be found here:

Current COVID 19 related activities of the Humanitarian OpenStreetMap Team (HOT), Red Cross Red Crescent Climate Center and HeiGIT are featured in a blog series around Forecast-based Financing.

The blog post,Anticipating and addressing epidemics – the potential of open data initiatives, provides an overview on how Missing Maps, Forecast-based Financing, HOT, Climate Center and HeiGIT efforts complement each other and how the established collaboration can help to better assess and limit the impacts of disasters and epidemics.

Our research group already supports the Missing Maps initiative since 2015. Among other things, the membership enabled us to learn about the requirements and challenges of the Red Cross organizations, MSF and other partners. Over the last couple of years, GIScience/HeiGIT furthermore strengthened and formalized the collaboration with German Red Cross, which recently also joined Missing Maps. Missing Maps in general focuses on preparatory mapping and on “putting the world´s vulnerable people on the OpenStreetMap”. Therefore, Missing Maps and the related work is also of outmost importance for the Red Cross organizations and their Forecast-based Financing program.

The Forecast-based Financing (FbF) programme was launched to allow for access to humanitarian funding already in the preparedness phase of the disaster cycle. The funding is used to generate in-depth forecast information and risk analysis and to resepctively plan early actions. Through this preparedness activities, disasters and their impact can be better anticipated and effects can be reduced.

We are looking forward to further explore the link of FbF, OSM mapping efforts and related services and how to make use of joint efforts to enable support in the response to epidemics and beyond.

Find here a new update of the OSMlanduse.org map. By injecting known tags provided by OpenStreetMap (OSM) into a remote sensing feature space using deep learning, tags were predicted when absent thus creating a contiguous map - initially for the member states of the EU. By design our method can be applied when- and wherever OSM and Copernicus data is available. Now we eye application for full continental coverage, other continents, and land use evolution. Improvements related to initial processing errors will be deployed soon. Insights will be provided in an upcoming publication authored by researchers Michael Schultz, Hao Li, Zhaoyan Wu, Daniel Wiell, Michael Auer and Alexander Zipf.

Among others, in collaboration with the Joint Research Center (JRC), Ispra and International Institute for Applied Systems Analysis (IIASA) the map is subjected to a online validation campaign that is launched during the EU Regions week the validation event will be initialized on Tue 14, October 2020, 9:30 by Michael Schultz of GIScience Research Group Heidelberg University and Ana-Maria Raimond of IGN France.

Our map is the first successful large area fusion of OSM and Copernicus at 10m spatial resolution or higher, where we acknowledged varying label noise and feature space quality, scales and effective use of artificial intelligence and computing. Our method solely relies on openly available data streams and does not depend on additional expert knowledge.

Brief method outline:

  1. OSM key value pairs (tags) were translated into Coordination of Information on the Environment (CORINE) land cover (CLC) land use (LU) classes and used as training labels
  2. Preprocessed Sentinel 2 RGB 10m data for EU was provide from Food and Agriculture Organization (FAO) and used as a feature space
  3. 1) and 2) were combined to produce a CLC classification of EU using deep learning
land use of Europe, Heidelberg and a countryside in Utrecht

Examples of the novel GIScience OSMlanduse land use product, from left to right: land use of Europe, Heidelberg and a countryside in Utrecht

The map is developed, deployed and hosted with support from HeiGIT (Heidelberg Institute for Geoinformation Technology).

Related work:

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