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From 13 to 17 September 2021, the “International Transdisciplinarity Conference ITD21” took place in online format. The TdLab Geography participated with the contribution “Towards a co-design of adaptation measures to heat events in cities: examples from Heidelberg, Germany”. In a self-produced video, Nicole Aeschbach and Kathrin Foshag presented two studies on climate change adaptation from current research at TdLab Geography. The video takes viewers on a tour to selected research sites in Heidelberg and reflects inter- and transdisciplinary approaches on heat adaptation strategies in cities (link to video at the end of this article). In a 45-minute zoom session, Nicole Aeschbach and Kathrin Foshag introduced their research in a pitch and discussed it with other participants.
The conference was titled “Creating Spaces and Cultivating Mindsets for Learning and Experimentation” and was hosted by the “Swiss Academies of Arts and the Sciences” and the “USYS TdLab” at ETH Zurich. A total of over 500 participants from the international transdisciplinarity community attended the conference.
Link to the conference video


TdLab Geographie mit einem Beitrag auf der ITD21-Konferenz

Von 13. bis 17. September 2021 fand die „International Transdisciplinarity Conference ITD21“ im Onlineformat statt. Das TdLab Geographie war mit einem Beitrag “Towards a co-design of adaptation measures to heat events in cities: examples from Heidelberg, Germany“ vertreten. In einem selbst produzierten Video stellten Nicole Aeschbach und Kathrin Foshag zwei Studien zur Klimawandelanpassung aus der aktuellen Forschung am TdLab Geographie vor. Das Video führt die Betrachter an ausgewählte Forschungsstandorte in Heidelberg und reflektiert inter- und transdisziplinäre Ansätze zur Hitzeanpassung in Städten (Link zum Video am Ende des Artikels). In einer 45-minütigen Zoom-Session hatten Nicole Aeschbach und Kathrin Foshag die Gelegenheit, ihre Forschung in einem Pitch zu präsentieren und mit den Teilnehmer*innen zu diskutieren.
Die Konferenz stand unter dem Motto “Creating Spaces and Cultivating Mindsets for Learning and Experimentation” und wurde ausgerichtet von den „Akademien der Wissenschaften Schweiz“ und dem „USYS TdLab“ an der ETH Zürich. Insgesamt waren über 500 Teilnehmer*innen aus der internationalen Transdisziplinaritäts-Community in den zahlreichen Sessions aktiv.
Link zum Konferenzvideo

Urban green spaces (UGSs) can provide important ecosystem services for citizens and their well-being. To make use of these services according to UGS user demands, urban residents, tourists, and city administrations should know where UGSs are located, what qualities they have and how to reach them on convenient routes. A new open access paper at the 6th International Conference on Smart Data and Smart Cities based on the results of the MeinGrün project presents a novel digital infrastructure which combines and fuses different data to map UGSs and their qualities, and makes this information available in a web app.
The interactive information service of the app aims to support citizens to explore and search for suitable UGSs and to provide routing options to reach them based on their preferences. Via implicit and explicit feedback functions included in the app, further information on UGS users’ preferences can be collected to enhance the overall knowledge basis, while respecting data privacy issues. The underlying data base consists primarily of open and volunteered geographic data (VGI), which allows for transferability to other cities.
The paper describes the system design, its backend and front-end components, as well as the process of development and deployment of the system in two pilot cities. Preliminary results of the piloting in the two cities (Heidelberg and Dresden) are presented, focusing on user preferences for UGSs searches. The added value of the web app for city residents and the role of the newly gained knowledge for urban planning is discussed and reflected upon.

Hecht, R., Artmann, M., Brzoska, P., Burghardt, D., Cakir, S., Dunkel, A., Gröbe, M., Gugulica, M., Krellenberg, K., Kreutzarek, N., Lautenbach, S., Ludwig, C., Lümkemann, D., Meinel, G., Schorcht, M., Sonnenbichler, A., Stanley, C., Tenikl, J., Wurm, M., and Zipf, A.: A WEB APP TO GENERATE AND DISSEMINATE NEW KNOWLEDGE ON URBAN GREEN SPACE QUALITIES AND THEIR ACCESSIBILITY, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VIII-4/W1-2021, 65–72.
https://doi.org/10.5194/isprs-annals-VIII-4-W1-2021-65-2021, 2021.

R. Hecht/IÖR-Media

ACKNOWLEDGEMENTS The research is supported by the German Federal Ministry of Transport and Digital Infrastructure (BMVI) under the frame of mFUND, a research initiative funding R&D projects related to digital data-based applications for Mobility 4.0 (grant number 19F2073A). We would like to thank the administrations and citizens of the pilot cities for their support.

Further news http://giscienceblog.uni-hd.de/tag/meingrun/

The meinGrün app https://meingruen.org/
Openrouteservice serves the general public since 2008.
A new follow-up project (HEAL) deals with heat-stress avoiding shady routing.

Related publications

MapSwipe has ben announced as ‘App of the Day‘ in the Apple AppStore! (27.08.2021)

Thank you to all our volunteers for the continued help and support which keeps the app going.

Read the blogpost here: https://apps.apple.com/ca/story/id1559236909

The team at the Heidelberg Institute for Geoinformation Technology (HeiGIT) and the GIScience Research Group at Heidelberg University has shaped MapSwipe’s development from the very beginning by designing the crowdsourcing approach behind MapSwipe, providing the tools needed to manage such a global project and make use of the data in timely manner. As part of the Missing Maps project, MapSwipe is a mobile app that was created to crowdsource map data from a network of global volunteers - just one swipe at a time. Individuals, volunteers from communities all over the world, can swipe through the app and tap areas where they find crucial infrastructure such as buildings and roads, identify changes in areas.

MapSwipe is an open source project built and maintained by volunteers, with the support of the British Red CrossHeiGIT and the GIScience Research GroupHumanitarian OpenStreetMap Team and Medecins Sans Frontieres. The projects have supported a range of missions and global organizations, such as the Red Cross Red Crescent Movement and Medecins Sans Frontieres, as well as local NGOs like the Tanzania Development Trust and MapPH.

Once a project has been requested by a community, the MapSwipe team creates it in the app, using imagery from a variety of sources and creating instructions that help the user to understand what to look for and the resulting action they should take. Each set of imagery is viewed by at least 3 individuals to improve data-quality. Users can track their impact, receiving badges for the distance swiped.

Through the research at HeiGIT and GIScience Research Group Heidelberg University it will soon be able to use machine-learning technologies to improve the open mapping. HeiGIT provides an API for enriched data sets based on MapSwipe results for humanitarian organization, develops and researches new project types such as for change detection, building validation or improving data completeness and also develops tutorials to help users contribute better data, to name a few Heidelberg contributions since the initial concept development. It is also used in our current research projects, such as LOKI or UnderCoverEisagenten

The team behind MapSwipe wants to send a big “Thank You” to all our passionate volunteers that keep on swiping day to day and help us filling the missing maps.

More MapSwipe related news: http://giscienceblog.uni-hd.de/tag/mapswipe/

Hello and welcome back to the ohsome Region of the Month-blog post series where you can read about potential use cases of the ohsome API and maybe even get inspired to send some requests of your own. If you are new to the series you should definitely take a look at former blog posts, for example our last one on the length of coastlines or this one which is about forest-tags too and was done in a jupyter notebook. This weeks blogpost looks at different “tree-related” tags in OpenStreetMap and their development in different countries over time (2008-2021). The tags of interest are “natural=wood“, “landuse=forest” and “landcover=trees“. Let’s begin!


For sending requests to the ohsome API you need input boundaries first, be it a GeoJSON (bpolys) or the coordinates of a region of interest (bboxes). As usual, we sent a “bpolys-request” and got our data from osm-boundaries. For convenience, you can find a GeoJSON-file with all the boundary data used in this analysis in a snippet here as well as the requests sent themselves.


Since there was a need for more than one filter-condition, several requests were sent. The conditions used are listed below:

  • filter1 = geometry:polygon and natural=wood
  • filter2 = geometry:polygon and landuse=forest
  • filter3 = geometry:polygon and landcover=trees
  • time = 2008-01-01/2021-07-01/P1M

Data exploration

First of all, one can observe that different tags dominate in each region and furthermore it is important to note that according to the OpenStreetMap wikilanduse=forest” is often times used for maintained forest areas, but some mappers use it on any woodland so there is no clear definition. On the other hand there is the “natural=wood“-tag which is usually used for forests with limited to no forestry management so in these regions there might either be a certain form of landuse dominating or the tagging behaviours are locally different from each other. The third tag looked at in this analysis is “landcover=trees” which describes an area physically covered by trees independently of the degree of human involvement.  Unfortunately, we could only find information on imports and tagging conventions for Canada and Japanbut based on those one can assume that for Canada, “natural=wood” would be the dominating tag, and that for Japan “natural=wood” and “landuse=forest” display low/no and high maintenance woodlands as described earlier. For all the countries looked at within the analysis the “landcover=trees“-tag has occurred at some point but only in few countries the use of it increased considerably, so it seems there might be a development in some countries while others focus on the other two tags.

Below you can see several graphics displaying the development of tree-related tags between 2008 and 2021

When looking at Finland, there is a first strong increase of the “natural=wood“-tag between March and May 2011, however since then there has been a an increasing trend in area with a subsequent stagnation and a light decrease beginning in the second half of 2020. The “landuse=forest“-tag on the other hand starts with only slowly rising values until the time span between May and October 2019 when there is a strong jump in numbers. Ever since “landuse=forest” held the highest object-numbers for Finland. The “landcover=trees“-tag occurred first in December 2012 and increased until today yet in much lower manner (see additional graphic in the snippet).

Within Paraguay, “natural=wood” emerges as the dominant tag, but with a rather changeable trend, as there are always phases of alternating increase and stagnation, as well as a decline in values between May 2017 and December 2019. In contrast, “landuse=forest” shows an increase without collapses, but with area values at a considerably lower level. In addition, a data jump occurs between September 2019 and January 2020. The “landcover=trees” day proves to be the day with the lowest values and reaches its maximum in March 2019 and records a strong decrease in the following month. As a result, values start to rise again.

For Canada the dominating tag is, as was expected, “natural=wood” with notably strong increases during the first half of 2017 followed by a minor but notable collapse. Since then the rise was more moderate. The other two tags show much lower values, but “landuse=forest” is still higher in numbers than “landcover=trees“. The main jump in data is between February and May 2018 and again between December 2019 and March 2020. The “landcover=trees“-tag doesn’t occur until December 2015 and the tag is seldom used.

New Zealand barely uses the “landcover=trees“-tag, which only occurred in December 2019, yet there is a notable increase of it since May 2021. The tag “natural=wood” dominates and displays a strong rise in values from January to July 2013 after which it stays more or less on the same level with a minor decline in the values between July 2019 and September 2020. It is similar with “landuse=forest” although in this case a strong increase only takes place between March and July 2013 and again between May and June 2014. Subsequent to that these values too stay on about the same level.

For Morocco, “natural=wood” initially turns out to be the most represented tag. This was the case until October 2012 and since then the tag “landuse=forest” has been at the top. The values for “landcover=trees” are generally quite low in this case as well, however towards the end of the period under consideration there is an increase to the previous maximum value, which was reached from August 18.

Looking at the tag development for the area of Italy, “landuse=forest” is the most represented and shows a quite strong increase right at the beginning until May 2008. Subsequently, the trend flattens out until about January 2010. At this point, a phase of high increase of the area begins until September 2012. After this, the trend flattens out again. The tag “natural=wood” shows a more or less steady and moderate trend until October 2014. Since then, the values have tended to stagnate.  The last tag “landcover=trees” has been more strongly represented since February 2011, but, as in all cases, is relatively poorly represented. Since June 2013, no major in- or decreases have been recorded here.

At last, for Japannatural=wood” shows by far the highest count values as well as a strong increase between March 2010 and January 2011, when they peaked too. Regarding the information on tagging conventions we were able to find and under the premise that everything was tagged according to them of course, it appears that in Japan most (tagged) woodland is low to no maintenance area.  Current values are getting a little closer to this maximum after declining subsequent to the peak. The “landuse=forest“-tag does not display any notable increases after January 2011 and values are beginning to slightly decrease by January 2019. The first occurrence of “landcover=trees” is in January 2017 but in comparison to the other tags the values are quiet low.

Finally, it can be summarised that “landcover=trees” has only very low values in all cases, whereas Paraguay and Italy in particular can show comparatively high values when considered individually. Moreover, it should not go unmentioned that “landcover=forest” and “natural=wood” alternate as the dominant tag depending on the region considered. In many cases, Italy being the exception, however, “natural=wood” is usually more strongly represented at first and is only surpassed by “landuse=forest” later in the course.

Thanks for reading this ohsome Region of the Month blogpost, we hope you liked it. Stay tuned for more content from this series in the future!

Background info: the aim of the ohsome OpenStreetMap History Data Analytics Platform is to make OpenStreetMap’s full-history data more easily accessible for various kinds of OSM data analytics tasks, such as data quality analysis, on a regional, country-wide, or global scale. The ohsome API is one of its components, providing free and easy access to some of the functionalities of the ohsome platform via HTTP requests. Some intro can be found here:

In the SYSSIFOSS project, we are investigating how we can take advantage of virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, in forestry applications. These applications include survey planning and optimization, sensitivity analyses, and algorithm development. For example, VLS may be used to investigate the influence of different sensor and platform configurations on the resulting point cloud and the forestry information that can be obtained from it. Furthermore, VLS data with perfect ground truth may serve as training and testing data for machine learning in application fields such as tree detection or tree segmentation. With the Heidelberg LiDAR Operations Simulator HELIOS++, we developed an open-source tool for VLS.

One important requirement for meaningful VLS is an appropriate representation of the objects, in this case of trees. In our brand new paper, we present our results from investigating different voxel representations of trees for VLS.

Workflow of the study.

Workflow of the study.

These representations include fixed-sized voxels of different voxel sizes and voxels scaled by local plant area density estimates.

Point clouds and voxel models.

Tree point clouds (a, d) and corresponding models with fixed-sized voxel cubes of 0.25 m side length (b, e) and dynamically scaled voxel cubes with additional shift to CoG (c, f) for a target tree of F. sylvatica (top row) and P. abies (bottom row).

By mimicking our real ALS and ULS laser scanning acquisitions from 2019 (see this post and our PANGAEA dataset) in simulations, we analyzed how accurate point cloud and tree metrics can be estimated from simulated point clouds using different voxel representations.

Overview of metrics

Overview of metrics.

Simulations were performed with the open-source software HELIOS++, which is available on GitHub.

We found out that dynamically scaling voxels using voxel-based estimates of plant area density allows simulating realistic point clouds while using a relatively coarse voxel grid. This reduces computational load compared to a fine voxel grid of regular fixed-sized voxels.

Qualitative comparison of opaque voxel modelling approaches. CBH = Crown base height, CPA = Crown projection area, CoG = Centre of gravity.

Qualitative comparison of opaque voxel modelling approaches. CBH = Crown base height, CPA = Crown projection area, CoG = Centre of gravity.

The paper is available under open access and CC-BY 4.0 clause here: https://doi.org/10.1016/j.rse.2021.112641.

Weiser, H., Winiwarter, L., Anders, K., Fassnacht, F.E. & Höfle, B. (2021): Opaque voxel-based tree models for virtual laser scanning in forestry applications. Remote Sensing of Environment. Vol. 265, pp. 112641.

The accompanying data and code are available on heiDATA: https://doi.org/10.11588/data/MZBO7T.

SYSSIFOSS is a joint project between the Institute of Geography and Geoecology (IFGG) of the Karlsruhe Institute of Technology (KIT) and the 3DGeo Research Group of Heidelberg University. The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number: 411263134. Find further details about the SYSSIFOSS project on the project website, in recent blogposts, or on Twitter (#SYSSIFOSS).

Within the AHK-4D project, we continually monitor topographic change at the alpine rock glacier Äußeres Hochebenkar in Austria. Based on multitemporal and multi-source 3D point clouds at up to two-week acquisition intervals, we are developing methods to quantify the magnitudes and frequencies of individual processes of topographic change over varying timescales.

This year, we have also acquired a UAV-borne photogrammetry dataset for the fist time, alongside with the extension of the existing TLS and UAV-borne laser scanning time series. As in the past years, we conducted a joint field campaign together with colleagues from the University of Innsbruck.

An interactive visualization of the brand new photogrammetric point cloud is available here:

A first check of the new dataset shows huge surface change on a ridge structure above the rock glacier front (see below) - valuable input for further development of our methods on 3D/4D change analysis - check them out, they might be interesting for you research as well!

Surface change between 2019 (yellow) and 2021 (white) shown in TLS point cloud cross section (upper picture). The area is located at a ridge structure above the rock glacier front (lower picture).

Surface change between 2019 (yellow) and 2021 (white) shown in TLS point cloud cross section (upper picture). The area is located at a ridge structure above the rock glacier front (lower picture; photo from July 2021).

Find our latest publications on methods of 3D/4D change analysis using rock glacier datasets below:
Ulrich, V., Williams, J.G., Zahs, V., Anders, K., Hecht, S., Höfle, B. (2021): Measurement of rock glacier surface change over different timescales using terrestrial laser scanning point clouds. Earth Surface Dynamics. Vol. 9, pp. 19-28.
Williams, J.G., Anders, K., Winiwarter, L., Zahs, V., Höfle, B. (2021): Multi-directional change detection between point clouds. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 172, pp. 95-113.
Winiwarter, L., Anders, K., Höfle, B. (2021): M3C2-EP: Pushing the limits of 3D topographic point cloud change detection by error propagation. ISPRS Journal of Photogrammetry and Remote Sensing, 178, pp. 240–258.
Zahs, V., Hämmerle, M., Anders, K., Hecht, S., Rutzinger, M., Sailer, R., Williams, J.G., Höfle, B. (2019): Multi-temporal 3D point cloud-based quantification and analysis of geomorphological activity at an alpine rock glacier using airborne and terrestrial LiDAR. Permafrost and Periglacial Processes. Vol. 30 (3), pp. 222-238.
Want to use 3D rock glacier data for your research? There is already open access TLS data of the rock glacier available.

The acquisition of UAV-borne laser scanning data was supported by the 4EU+ collaboration project Towards sustainable development of natural environments based on continuous remote sensing monitoring.

We are happy to announce a revamped openrouteservice maps client for disaster management. It was built based on the new openrouteservice maps client, that is an open source route planner with plenty of features. Specific disaster features were incorporated via the development of developing plugins. It was developed and is maintained by the Heidelberg Institute for Geoinformation Technology (HeiGIT gGmbH) together with the GIScience Research Group at Heidelberg University.

Thanks to the work of the Humanitarian OpenStreetMap Team and the Missing Maps project, in disaster situations the OSM data is continually updated and enriched with critical information. Thus the road network is constantly enlarged by thousands of volunteers. The Openrouteservice for Disaster Management has the exceptional ability to take this data source update into account and recalculates the routing graph once an hour on basis of the most current OSM data. Furthermore, it considers as passable and impassable tagged ways (impassable=yes or status=impassable) and dynamically adjusts the graph weights of OSM streets accordingly. This allows disaster responders to navigate on basis of the most current road network. Additionally, Openrouteservice for Disaster Management also provides an accessibility analysis service for a given location and the possibility to export for exporting and importing files that can be used offline in mobile devices.

Fig. 1: New ORS for disaster management maps client

A new disaster client, with new features

The new disaster maps client is a brand-new piece of software that uses a modern and progressive JavaScript framework (VueJS) and up-to-date components. Besides the rewriting of the client, several new features were introduced, if we take as reference the previous disaster client. Many of the new features available were brought about by the new ORS base maps client, on which the new disaster client is based.

New features that are specific to the new disaster client:

Improved region selector

In the new disaster client, on the first app load the user will be asked to select a disaster region and once one is selected, the modal window will be automatically closed and the target region will be focused. After a region is defined, an smaller region selector button is available on the bottom-left corner. This button uses less space then then the previous client region selector that was permanently placed over the map.

Selected region synchronized with URL

When a disaster region is selected, and a route or isochrone is calculated, the target region is synchronized with the application URL so that if the app is reloaded or the URL is shared, the application can automatically (re)select the disaster region, calculate a route/isochrone and then display the exact same view that was visualized with the same URL. In the previous disaster client this would not work.

Plugin-based features

In the new disaster client, the features that differentiate it from the base maps client are implemented via the creation of plugins. These plugins do not affect the base maps client code, but listen to existing hooks and add custom features in certain key-points. This architecture allows the new disaster client to get frequent updates from the base client with minimum effort.

New features brought about by the new base maps client:

Embedded mode

The embedded mode allows the embedding of any map view (place, directions, isochrones). It is mode is designed to allow developers or website editors to embed a map view that is semi-interactive in a view-only mode. The users can go to a full interactive map via a “view on ORS” button.

Friendly and meaningful URL

Use a URL structure that is more friendly ad meaningful allowing users to easily understand the app mode (place, directions and isochrones) and the places that are being used.

Search mode

Allows searching for nearby places given a center. It provides a way to see more results for a given input when the suggestions don’t contain what the user is looking for, or when a map view is more useful to decide which venue/place matches the user needs.

Place mode

Allow to search, select and show only a place on the map view. This place centered view can be shared and it also uses and auto zoom strategy that is based on the place layer type (a bigger zoom for a street and a smaller one for a country, for example). The place view also shows the boundaries of the selected place if it is a city, a region/state or a country.

Enhanced search suggestions

The places shown on the suggestion list as the user types something on the place input uses an enhanced strategy to display the items that is expected to match the expectations of most of the users by combining venues, address and cities with different levels of priority.

More advanced settings

Some additional settings were added, allowing the user to have more control of some behaviors of the app, like custom tiles provider, compress URL data and auto-fit bounds when route changes. Check the settings/advanced to see all the options.

URL shortcuts

Provide shortcuts to the settings and about pages, so that the user can receive some instructions that start with the settings page open. It is also helpful when explaining how to adjust some app settings to new users. To access settings just go to https://maps.openrouteservice.org/#/settings.

Auto select the only address

When the user types a full address and only one result is expected to be returned, the user can immediately hit enter/return and the app will then run a geocode search, convert the address text to a coordinates based place and auto select the place as the origin/destination/stop according to the context. This is useful for cases when the users are not searching for places, but know which exact address they are looking for and want to skip some steps.

Rebuild avoid polygons on URL reload

The avoid polygons created via avoid polygon drawing tool are rebuild and displayed again when a directions URL is reloaded or shared with someone else.

Accessibility mode

Allow users to move the map view with arrow buttons and highlight the active inputs visually. It is intended to be used by users with special needs.

Instant dragging visual feedback

When a route is shown on the map the users can interact with it by dragging a point and an instant visual feedback will be displayed according to the move. Although the contour of the dragged route might not be kept after a new route is calculated by the service when the user finishes editing it, it works like a white noise, showing that the app is responding to the user commands.

Distinct iconography for some types of place

Some results displayed in the list of suggestions when something is typed have specific iconography so that the users can easily distinct between some types of places, like cities, regions/states and countries.

Map view markers interactivity

The markers plotted on the map view offer some actions that can be accessed via mouse when they are clicked, like delete (a new route is calculated or the app goes to place mode if only one marker is left) and toggle direct, that will skip a calculated route between the selected place and the next point of the route.

Save last active profile

When the user changes the active profile (like bike, car, wheelchair) this profile is saved in the browser’s local storage and the same profile will be loaded/set as active when the maps client is loaded again. So users that uses more often a profile will not have to re-select a profile every time s/he wants to load the app and calculate a route.



Neis, P. & Zipf, A (2008): OpenRouteService.org is three times “Open”: Combining OpenSource, OpenLS and OpenStreetMaps. GIS Research UK (GISRUK 08). Manchester.

Ludwig, C., S. Fendrich, T. Novack, S. Marx, A. Oleś, S. Lautenbach, A. Zipf (2020): Optimal ans Ziel: Routing-Dienste auf Basis nutzergenerierter Geodaten – Herausforderungen und Lösungsansätze für globale Datensätze, In: Zagl, Loidl (Hrsg.): Geo-IT in Mobilität und Verkehr, Geoinformatik als Grundlage für moderne Verkehrsplanung und Mobilitätsmanagement. VDE Wichmann Verlag. S. 89-106.

OpenStreetMap (OSM) is a global mapping project which generates free geographical information through a community of volunteers. OSM is used in a variety of applications and for research purposes. However, it is also possible to import external data sets to OpenStreetMap. The opinions about these data imports are divergent among researchers and contributors, and the subject is constantly discussed. The question of whether importing data, especially large quantities, is adding value to OSM or compromising the progress of the project needs to be investigated more deeply. For a recent study by Witt et al. published Open Access, OSM’s historical data were used to compute metrics about the developments of the contributors and OSM data during large data imports which were for the Netherlands and India. Additionally, one time period per study area during which there was no large data import was investigated to compare results. For making statements about the impacts of large data imports in OSM, the metrics were analysed using different techniques (cross-correlation and changepoint detection). It was found that the contributor activity increased during large data imports. Additionally, contributors who were already active before a large import were more likely to contribute to OSM after said import than contributors who made their first contributions during the large data import. The results show the difficulty of interpreting a heterogeneous data source, such as OSM, and the complexity of the project. Limitations and challenges which were encountered are explained, and future directions for continuing in this field of research are given.

In this study, only the time period was investigated where approximately 80% of the data were added. Therefore, potential influences and impacts outside the observation periods have not been considered.
For the computation of the contributor activity, all contribution types (i.e., creation, deletion, tag changes and geometry changes) were included to get a general understanding of the number of active users per timestamp. As a consequence, contributors who were deleting data were weighted as much as users who were creating or updating elements.
The changepoint detection was used to compute the most significant changepoint in the observation period. For most of the imports, this approach provided useful results which showed the distinct changes of the contributor activity during or after the import. However, for imports such as the 3dShapes import or the BAG import, the development of the user activity was more complex. During the 3dShapes import, the contributor activity was increasing with the start of the import, and dropped down after most of the data were imported. Then, the contributor activity increased again. A similar pattern could be seen during the BAG import, where the contributor activity increased throughout the conduction of the import but decreased afterwards. By computing only one changepoint, these processes were simplified. Additionally, other OSM events could have influenced the changepoint detection. However, this problem is omnipresent when working with OSM data.
The algorithm for finding peaks in the development of contribution types used a multiple of the mean as the detection criterion. Therefore, other OSM events which happened in the same time period might have been detected as a peak, even though they were maybe not related to the import. Moreover, also peaks that happened before an import but within the observation period were counted. Additionally, more analysis is needed to investigate the peaks in more detail to ensure that the peaks are directly related to the import.
For the import, dedicated user accounts have to be used for importing data into OSM. These accounts were included in the results. Moreover, the differences in the total number of contributors who were involved in the imports (e.g., AND import in India with 18 contributors; AND import in the Netherlands with 108 contributors) have to be considered when evaluating and comparing results.
Furthermore, this study did not distinguish between different types of large imports (e.g., automatically or manually conducted imports, or a combination of both). The quantity of imported data was the only criterion for the selection.
This study presented a first approach for getting a deeper understanding about the impact of large data imports in OpenStreetMap by investigating large data imports in the Netherlands and India.
The results were manifold. It was found that for most of the large data imports which were analysed, the contributor activity increased during or after the conduction of the import. Looking at the imports in the early stage of OSM, especially the AND imports in 2007 and 2008, one can see that significantly more contributors were active than before. Imports which happened at a later stage did not show such a strong impact. During the BAG import in the Netherlands and the building import in India, the number of contributors increased. However, after most of the data were imported, the contributor activity slightly decreased again. Nonetheless, one can see that during a large data import the number of unique active contributors rose.
The analysis of the contributor engagement pointed out that the majority of users who were involved in an import were import-inspired; i.e., their first contributions happened during an import. Again, this finding supports the argument that with large data imports, more contributors were actively joining the project. However, mappers who were active beforehand were more likely to keep contributing in the time after the import was concluded. Therefore the study showed that already activate mappers were not driven away from the project. This study did not differentiate between dedicated user accounts which were created only for importing data and regular user accounts which need to be considered when reasoning about the findings.

Regarding the contribution patterns and the development of tag keys, no specific impact of large data imports was found in this study. The number of unique tag keys increased as the number of elements increased, given that external information was mapped to OSM tags. More research is needed to understand how the community is changing OSM data after a large data import.

The study considered the impact of large data imports from a data perspective on a small subset of imports that were conducted. For future research, the analysis of different data imports might also incorporate other aspects of OSM—for example, community events or mapping events and how they are related to imports. The investigation of automated processes, e.g., scripts or bots, could lead to better understanding about how large chunks of imported data are changed. Moreover, the phase of OSM in which an import is conducted could be analysed more thoroughly. This might help to understand if an import could be performed to also support the establishment or growth of a community in a specific region. Additionally, in that regard, the effect of the media or the OSM community creating awareness about data donations and respective data imports needs to be investigated. Additionally, the analysis of OSM contributors could be extended, for example, by considering the locations of contributors who are involved in an import process. Emerging spatial patterns could help to understand how local communities are developing during an import. The attributes of imported elements and how they are evolving over time could be analysed with a focus on the semantics of the data.

Witt, R.; Loos, L.; Zipf, A. (2021): Analysing the Impact of Large Data Imports in OpenStreetMap. ISPRS Int. J. Geo-Inf. 2021, 10, 528. https://doi.org/10.3390/ijgi10080528


Selected earlier & related work:

A recently published article in ‘Current Opinion in Environmental Sustainability‘ investigates the role of digital technologies and data innovations, such as big data and citizen-generated data, to enable transformations to sustainability. We reviewed recent literature in this area and identified that the most prevailing assumption of work is related to the capacity of data to inform decision-making and support transformations. However, there is a lack of critical investigation on the concrete pathways for this to happen. We present a framework that identifies scales and potential pathways on how data generation, circulation and usage can enable transformations to sustainability. This framework expands the perspective on the role and functions of data, and it is used to outline a critical research agenda for future work that fully considers the socio-cultural contexts and practices through which data may effectively support transformative pathways to sustainable development.

The review, conceptual framework and critical research agenda laid down in this article offer a starting point to develop a deeper understanding of the relation between data innovations and transformations to sustainability which includes broader considerations of social, cultural and political issues. Building upon our proposed framework and critical agenda, future research should investigate how specific projects on sustainability transformations are able to integrate the different data transformation pathways and functions we identified, whilst addressing the corresponding tensions and challenges. We thus hope this article can inspire future research and practical projects to consciously reflect about their assumptions and practices to be able to effectively integrate data into sustainability transformation processes.

De Albuquerque, J. P.,  L. Anderson, N. Calvillo, J. Coaffee, M.  Cunha, L. Degrossi, G. Dolif, F. Horita, C. Klonner, F. Lima-Silva, V. Marchezini, M .H. da Mata Martins, D.-Grajales, V. Pitidis, C. Rudorff, N. Tkacz, R. Traijber, A. Zipf (2021): The role of data in transformations to sustainability: a critical research agenda.
Current Opinion in Environmental Sustainability
. Vol. 49, 2021, pp 153-163.

This article is part of the project Waterproofing Data that is supported by the Belmont Forum and NORFACE Joint Research Programme on Transformations to Sustainability (https://www.norface.net/program/transformations-to-sustainability/), which is co-funded by DLR/BMBF, ESRC/GCRF (ES/S006982/1), FAPESP (process n. 18/50039-4) and the European Commission through Horizon 2020.

Selected project related publications:

Our forest laser scanning dataset collected in the frame of the SYSSIFOSS project is now openly available through the PANGAEA Data Publisher:

Weiser, H., Schäfer, J., Winiwarter, L., et al. (2021): Terrestrial, UAV-borne and airborne laser scanning point clouds of central European forest plots, Germany, with extracted individual trees and manual forest inventory measurements. PANGAEA, https://doi.org/10.1594/PANGAEA.933426

Until the DOI is registered, the dataset can be accessed here.

In 2019 and 2020, we acquired 3D forest data with laser scanners mounted on three different platforms: an aircraft, an uncrewed aerial vehicle (UAV) and a ground-based tripod. The point cloud datasets differ in their coverage, resolution and view directions. Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) was conducted during summertime under leaf-on conditions. UAV-borne laser scanning (ULS) was performed both in summer and early spring, resulting in 3D point clouds of forests under leaf-on and leaf-off conditions. From the plot level datasets, we extracted point clouds of individual trees. We then computed tree properties from the point clouds, such as diameter at breast height (DBH), tree height or crown diameter. We measured the same properties in-situ, and identified the species of every tree.

Point clouds of a European Beech, coloured by reflectance. a) ALS point cloud acquired under leaf-on conditions, b) ULS point cloud acquired under leaf-on conditions, c) ULS point cloud acquired under leaf-off condition, d) TLS point cloud acquired under leaf-on conditions. ALS = Airborne laser scanning, ULS = UAV-borne laser scanning, TLS = Terrestrial laser scanning.

Point clouds of a European Beech, coloured by reflectance. a) ALS point cloud acquired under leaf-on conditions, b) ULS point cloud acquired under leaf-on conditions, c) ULS point cloud acquired under leaf-off condition, d) TLS point cloud acquired under leaf-on conditions

Our final dataset encompasses 1491 trees. For the majority of trees, both ALS and ULS data is available. For 249 trees, point clouds from all three platforms are provided. Besides the larger area and the individual tree point clouds, we also provide the flight trajectories and scan positions, as well as all relevant metadata about the acquisitions and a documentation of our processing workflow.

Within our research project, we are building a database of the individual trees, which we will use to create synthetic 3D forest stands based on 2D forest inventory information. These scenes will be virtually scanned in HELIOS++, the Heidelberg LiDAR Operations Simulator. This way, we can create an abundance of realistic laser scanning datasets of forests with varying sensor configurations, acquisition settings and forest structures. Such simulated point clouds have numerous research applications, such as survey planning, algorithm development and machine learning.

SYSSIFOSS is a joint project between the Institute of Geography and Geoecology (IFGG) of the Karlsruhe Institute of Technology (KIT) and the 3DGeo Research Group of Heidelberg University. The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number: 411263134. Find further details about the SYSSIFOSS project on the project website, in recent blogposts, or on Twitter (#SYSSIFOSS).

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