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Todays data production, maintenance, and use have changed in the last years.  While these tasks were reserved to professionals until a few years ago, the situation has changed.  This is no different in the geographical domain. Volunteers gather general information in Wikipedia and geographical information in OpenStreetMap.  Twitter users provide not only text snippets but in some cases also their current coordinates.  Whenever people interact in the production, maintenance, or use, they become part of a social process, leading to a new form of data sources.  Many terms used in this context (VGI, UGC, etc.) have some connotation by the way the are used.  Our journal article ‘Shared Data Sources in the Geographical Domain’ examines such social data sources in general without restricting to a certain domain.

We coin the term ‘Shared Data Sources’ as a generic umbrella term without any connotation that is often inherent to other terms: ‘A dataset or project is called a “Shared Data Source” (SDS) if its production, maintenance, and use are predominantly social processes.’ In addition, ‘we coin the term “Geographical Shared Data Sources” (GSDS) for referring to Shared Data Sources in the geographical domain.’ Feel free to do use these terms when referring to all these datasets.  Contributors and users share these datasets in various ways, they have become social!

Existing Shared Data Sources are often discussed in the context of Volunteered Geographic Information (VGI), Ambient Geographic Information (AGI), and Participatory Geographic Information (PGI).  We use these (proto)types to set Shared Data Sources in their mutual context.  The Triangle of Shared Data Sources (see above) is only one such example.  Our journal article contains some more such visualizations.

Mocnik, F.-B., Ludwig, C., Grinberger, A.Y., Jacobs, C., Klonner, C., Raifer, M. (2019): Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques ISPRS International Journal of Geo-Information 8(5), 242.

Related articles:

Mocnik, F.-B., Zipf, A., Raifer, M. (2017): The OpenStreetMap Folksonomy and Its Evolution. Geo-spatial Information Science 20(3), 219–230.

Mocnik, F.-B., Mobasheri, A., Griesbaum, L., Eckle, M., Jacobs, C., Klonner, C. (2018): A Grounding-Based Ontology of Data Quality Measures. Journal of Spatial Information Science 16, 1-25.

Mocnik, F.-B., Mobasheri, A., Zipf, A. (2018): Open Source Data Mining Infrastructure for Exploring and Analysing OpenStreetMap. Open Geospatial Data, Software and Standards 3(7).

Can you imagine how much sand is being moved on the beach in the course of a week? Did you ever observe truckloads of sand being transported on the beach in the absence of storms and bulldozers? It is hardly possible to estimate to the naked eye, but can be quantified with permanent terrestrial laser scanning (TLS).

This new paper investigates how the temporal interval of TLS acquisitions influences volume change observed on a sandy beach regarding the temporal detail of the change process and the total volume budget, on which accretion and erosion counteract. The study uses an hourly time series of TLS point clouds acquired over six weeks in Kijkduin, the Netherlands. Results of the hourly analysis are compared to those of a three‑ and six‑week observation period.

Results of change analysis for a three- and six-week period (left) and visualized as hourly time series of volume change for one location (right) (Anders et al. 2019)

Results of change analysis of sand on a beach for a three- and six-week period (left) and visualized as hourly time series of volume change for one location (right) (Anders et al. 2019)

For the larger, six-week period, a volume increase of 0.3 m³/m² is missed on a forming sand bar before it disappears, which corresponds to half its volume. Generally, a strong relationship is shown between observation interval and observed volume change. An increase from weekly to daily observations leads to a five times larger volume change quantified in total.

Another important finding is a temporally variable measurement uncertainty in the 3D time series, which follows the daily course of air temperature.

Will you be there? The research will be presented at the ISPRS Geospatial Week 2019 in the Change Detection session on Wednesday, 12th June 2019, 11:00 am - 12:30 am. The 3DGeo presents another research topic (Kumar et al. 2019) in the Machine & Deep Learning session.

Find all details in the full paper:

Anders, K., Lindenbergh, R. C., Vos, S. E., Mara, H., de Vries, S., and Höfle, B. (2019). HIGH-FREQUENCY 3D GEOMORPHIC OBSERVATION USING HOURLY TERRESTRIAL LASER SCANNING DATA OF A SANDY BEACH, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 317-324, DOI: 10.5194/isprs-annals-IV-2-W5-317-2019

The paper is the result of fruitful cooperation within the research projects CoastScan (Dr. Sander Vos, Prof. Roderik Lindenbergh, and Prof. Sierd de Vries) and Auto3Dscapes under lead of Prof. Bernhard Höfle (3DGeo) and Dr. Hubert Mara (FCGL).

An important part of the research was conducted during the research visit of Katharina Anders with the group of Prof. Roderik Lindenbergh in the Geoscience & Remote Sensing department at TU Delft. We thank HGS MathComp for supporting the three-month research visit of PhD student Katharina Anders at TU Delft!

Methods of 4D geospatial data analysis are the core research subject of the Auto3Dscapes project on Autonomous 3D Earth Observation of Dynamic Landscapes. Find out about the 3DGeo group’s work in further geomorphic settings, such as snow cover monitoring at Zugspitze mountain and rock glacier deformation in the Austrian alps.

To stay updated, follow us on ResearchGate!

The PhD project Auto3Dscapes is funded by the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp), founded by DFG grant GSC 220 in the German Universities Excellence Initiative.

A paper investigating the relevance of (pre-calculated) features for 3D point cloud classification using deep learning was just published in the ISPRS Annals of Photogrammetry and Remote Sensing.

The study presents a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compares it with an implementation of the state-of-the-art deep learning framework PointNet++. It is found that the classification accuracy improves by up to 33% when including normal vector features with multiple search radii and features related to spatial point distribution. The method achieves a mean Intersection over Union (mIoU) of 94%, outperforming PointNet++’s Multi Scale Grouping by up to 12%. The paper presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning.

The study uses point clouds from the semantic3D dataset, a labelled 3D point cloud data set of geographic scenes. The figure below shows the labelled 3D point cloud from the paper as ground truth and a result of the deep learning classifier for one of the feature sets (cf. Kumar et al. 2019).

Non-end-to-end deep learning point cloud classification (Kumar et al. 2019)

Result of non-end-to-end deep learning point cloud classification (Kumar et al. 2019)

Wonder how misclassifications in the result can be explained? Find all the details in the paper:

Kumar, A., Anders, K., Winiwarter, L., and Höfle, B. (2019). FEATURE RELEVANCE ANALYSIS FOR 3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W5, 373-380, DOI: 10.5194/isprs-annals-IV-2-W5-373-2019

The research will be presented at the ISPRS Geospatial Week 2019 at University of Twente in Enschede (NL) in the Machine & Deep Learning session on Wednesday, 12th June 2019, 9:00 am - 10:30 am.

Will you be there?

The 3DGeo presents another research topic (Anders et al. 2019) from the Auto3Dscapes project in the Change Detection session.

The 16th ISCRAM conference took place in the city of Valencia (Spain). It succeeded again to gather an international audience of researchers that shared their work all around crisis management.

As ISCRAM is mainly interested in the support and intersection of practical application in crisis management and research, the focus was however again on another group of actors: the practitioners and stakeholders. They coordinate efforts and need to take decisions on the ground- based on data and applications and related information. The ISCRAM conference is a great platform for the exchange of researchers and practitioners/stakeholders to allow for combined applications.

Our team from Heidelberg contributed in different forms to the ISCRAM. Martin Hilljegerdes gave a talk about effects of consecutive hurricane events on evacuation patterns, Melanie Eckle presented research about the demand and supply of humanitarian geodata use, and presented the Missing Maps project in scope of the workshop “Encouraging Productive Interaction between Practitioners and Researchers” and Carolin Klonner gave insights into a participatory approach based on sketch maps for group discussions about flood experiences.

The ISCRAM conference was a great opportunity to discuss our research with people from different other institutions and identify possible ideas to take our ideas further through sustainable collaborations- also in some special contexts like, e.g., the Paella Workshop that all participants were invited to join.

First ISCRAM Paella Workshop

ISCRAM at City of Arts and Science

Martin Hilljegerdes, & Ellen-Wien Augustijn-Beckers. (2019). Evaluating the effects of consecutive hurricane hits on evacuation patterns in Dominica. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 462–472). Valencia, Spain: Iscram.

Carolin Klonner, & Luca Blessing. (2019). Gathering Local Knowledge for Disaster Risk Reduction: The Use of Sketch Maps in Group Discussions. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 1397–1398). Valencia, Spain: Iscram.

Melanie Eckle, Sven Lautenbach, & Alexander Zipf. (2019). Towards bridging the gap between demand and supply in humanitarian geodata use. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management (pp. 1381–1382). Valencia, Spain: Iscram.

Very interesting terrestrial laser scanning and terrestrial and UAV image datasets of different Alpine sites in Austria have just been openly provided on PANGAEA. The data are related to the first (2015) and second (2017) edition of the Innsbruck Summer School of Alpine Research - Close Range Sensing Techniques in Alpine Terrain and can, for example, be used for surface change analyses between to epochs or the developement of new methods.

1) Multi-temporal terestrial laser scanning datasets acquired at the lower tongue area of the Äußeres Hochebenkar rock glacier, close to Obergurgl (2015, 2017)
  • Pfeiffer, J., Höfle, B., Hämmerle, M., Zahs, V., Rutzinger, M., Scaioni, M., Lindenbergh, R., Oude Elberink, S., Pirotti, F., Bremer, M., Wujanz, D. & Zieher, T. (2019): Terrestrial laser scanning data of the Äußeres Hochebenkar rock glacier close to Obergurgl, Austria acquired during the Innsbruck Summer School of Alpine Research. PANGAEA. DOI: https://doi.pangaea.de/10.1594/PANGAEA.902042.

2) Terrestrial and UAV images and point clouds and ground control points of the Rotmoos valley close to Obergurgl in Austria (2015, 2017)

  • Pfeiffer, J., Scaioni, M., Rutzinger, M., Adams, M., Graf, A., Sotier, B., Höfle, B., Lindenbergh, R., Oude Elberink, S., Pirotti, F., Bremer, M., Zieher, T., Hämmerle, M. & Wujanz, D. (2019): Terrestrial and unmanned aerial vehicle images of the Rotmoos valley bottom near Obergurgl, Austria with coordinates of corresponding ground control points acquired during the Innsbruck Summer School of Alpine Research 2015 and 2017. PANGAEA. DOI: https://doi.pangaea.de/10.1594/PANGAEA.898939.

3) Multi-temporal terrestrial laser scanning data of the Nesslrinna landslide and its surrounding close to Obergurgl, Austria (2015, 2017)

  • Pfeiffer, J., Wujanz, D., Zieher, T., Rutzinger, M., Scaioni, M., Höfle, B., Lindenbergh, R., Oude Elberink, S., Pirotti, F., Bremer, M. & Hämmerle, M. (2019): Terrestrial laser scanning data of the Nesslrinna landslide close to Obergurgl, Austria acquired during the Innsbruck Summer School of Alpine Research. PANGAEA. DOI: https://doi.pangaea.de/10.1594/PANGAEA.901293.

This June, the third edition of the Innsbruck Summer School of Alpine Research will take place in Obergurgl and the investigated sites might be revisited to extend the published datasets.

Interested in the Innsbruck Summer School of Alpine Research? Find more detailed information in the publications below:

  • Rutzinger, M., Bremer, M., Höfle, B., Hämmerle, M., Lindenbergh, R., Oude Elberink, S., Pirotti, F., Scaioni, M., Wujanz, D. & Zieher, T. (2018): Training in Innovative Technologies for Close-Range Sensing in Alpine Terrain. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. IV-2, pp. 239-246.
  • Rutzinger, M., Höfle, B., Lindenbergh, R., Oude Elberink, S., Pirotti, F., Sailer, R., Scaioni, M., Stötter, J. & Wujanz, D. (2016): Close-Range Sensing Techniques in Alpine Terrain. In: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. III-6, pp. 15-22.
P.S.: The area around Obergurgl (Äußeres Hochebenkar rock glacier, Rotmoos valley) has also been investigated regularily by the 3DGeo Research Group in recent years and will be revisited this summer.
Check out further open access data publications and tools of the 3DGeo group on our website.

Last week Dr. Clemens Jacobs successfully defended his PhD about methods for assessing the data quality of citizen science observations of organisms.

Congratulations, well done!!! We wish all the very best for the future!

The research aimed at using geographic context as an information source for estimating the plausibility of an observation, e.g., of a bird, which was reported to a citizen science portal such as iNaturlist or Artenfinder collecting such data from volunteers. To this end, approaches were developed which tap into different kinds of sources of Volunteered Geographic Information (VGI) to capture geographic context. One of these sources is OpenStreetMap (OSM). HeiGIT’s OpenStreetMap History Database (OSHDB) as part of the ohsome OpenStreetMap History Analytics platform was used to derive typical contexts of species in the form of OSM tags which are frequently found close to observations of certain species. This information can then be used to examine the tag context of a new observation of such a species. If the context of an observation fits the typical OSM context well, this indicates a high plausibility of that observation as explained in this post.

Some further results can also be found e.g. in this publication:

Jacobs C. and A. Zipf (2017): Completeness of Citizen Science Biodiversity Data from a Volunteered Geographic Information Perspective. Geo-Spatial information Science, GSIS, 2017. Taylor & Francis. DOI: 10.1080/10095020.2017.1288424.

The risk of exposure to heat and fine particel matter on human health has gained significant media coverage recently. The so called dieselgate and the ban of driving for older diesel vehicles in several German cities have renewed attention to the health risk of fine particle matter - for which traffic is a major emission source. While currently raising temperatures and sunny weahter are mainly preceived as a source of joy the last years have seen discussions about the health related effects of heat exposure especially for elderly.

A current study - that has been prepared under involvement of GIScience/HeiGIT member Sven Lautenbach - has investigated effects of both heat and fine particle matter (PM10) in Germany. The study followed an ecological approach and used a case-cross over design. The study period included the years 2002-2006. The relationship between the stressors and mortality was modelled both at the level of the federal state of Germany as well as at the level of 439 administrative units (kreisfreie Städte, Landkreise, Kreise, Stadtkreise,
Stadtverbände, Regionalverband Saarbrücken). The analysis was performed based on a logistic regression model that modelled the risk of mortality given the presence of heat (days with a heat-stress-index of more than 26.7°C) induced stress compared to days without that stress. For days with heat exposure the additional effect of PM10 was considered. Effects were controlled for sex and age - other control variables such as the day of the year or population density had no significant effect on the response in the study.

Resuts indicated significant effects of heat exposure (adjusted odds-ratio: 1.07) and PM10 (adjusted odds-ratio: 1.01). Risk of mortaility due to heat exposure and PM10 exposure did not differ significantly by sex. Interestingly, the analysis at the level of the federal state of Germany did not find a significant effect of age on the increase of risk due to the stressors. Put differently: the observed increase of mortality by heat and PM10 effected all age classes in a similar way. The analysis at the level of the 439 administrative units showed that risk varied significantly in space - highlighting the importance of follow-up studies that investigate the role of moderating factors.

Georgy, Sascha, Sven Lautenbach, Heiko J. Jahn, Lutz Katzschner, und Alexander Krämer. „Erfassung von hitze- und feinstaubbedingten Gesundheitsrisiken in Deutschland: Ein epidemiologischer Studienansatz“. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 20. Mai 2019. https://doi.org/10.1007/s00103-019-02960-8.

On May 1st, the SYSSIFOSS project has started and first test scans have already been conducted in the forest. Experciences from these initial testings serve as a basis for many field campaigns this summer which aim at the acquisition of high-density point clouds of 140 single trees with a RIEGL VZ-400 terrestrial laser scanner. Moreover, first scans have been acquired with a RIEGL miniVUX-1 UAV laser scanner, which is part of the 3DGeo group’s brand-new UAV-borne LiDAR system. These point clouds will be used for the validation of species-specific tree models derived from airborne laser scanning.

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.

In this project a new approach to create synthetic LiDAR data is suggested by combining the outputs of an established forest growth simulator with a to-be-created database of species-specific model trees extracted from real LiDAR point clouds. This approach will result in inventory information at the single tree level and a matching 3D forest structure for large areas.

Terrestrial laser scanning in a green and beautiful forest environment.

Terrestrial laser scanning in a green and beautiful forest environment.

Find more details on the SYSSIFOSS project on the project website and in recent blog posts.

The project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project number: 411263134.

TdLab Geography at ICCA 2019

Nicole Aeschbach (TdLab Geography / Transdisciplinarity Lab) participated in the ICCA 2019. The German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, the State of Baden-Württemberg and the City of Heidelberg jointly hosted the International Conference on Climate Action in Heidelberg on 22 and 23 May 2019. It was great to meet active people from local communities, regions and national governments from all over the world and to discuss good practices and tools for reaching the climate mitigation goals set in Paris 2015. On Friday, 24 May 2019, Nicole Aeschbach was invited to the City of Heidelberg’s Office of Environmental Protection, Trade Supervision and Energy (led by Sabine Lachenicht) to present her approach of transdisciplinarity in climate change research to Lisa Helps (Mayor of the City of Victoria, Canada) and Beate Weber-Schuerholz (former Mayor of the City of Heidelberg and former member of the European Parliament). It was an honor and a pleasure for the TdLab Geography to share and to exchange experiences and ideas.

Participants of the Youth Climate Summit and Luisa M. Neubauer and Jakob Blasel (Fridays for Future) during the opening session of the ICCA 2019

Participants of the Youth Climate Summit and Luisa M. Neubauer and Jakob Blasel (Fridays for Future) during the opening session of the ICCA 2019

Global Platform 2019 in Geneva

Creating maps helps humanity. Drawing maps together with communities is crucial for effective risk reduction interventions, ensuring no one is left behind.

The progress of the implementation of the targets set by the Sendai Framework for Disaster Risk Reduction (DRR) have been key discussion points during this years Global Platform in Geneva. Reducing disaster risks and losses in lives, livelihoods and health requires concrete actions by several stakeholders. Within the Missing Maps project we tackle this by creating maps in OpenStreetMap for places which today lack data entirely. During the Global Platform 2019 a team from the Heidelberg Institute for Geoinformation Technology (HeiGIT), the Humanitarian OpenStreetMap Team (HOT), the Netherlands Red Cross (510 global) and the International Federation of Red Cross and Red Crescent Societies (IFRC) presented the Missing Maps approach. We were also happy to finally welcome the German Red Cross (GRC) in our Missing Maps community.

Bring together Disaster Risk Reduction and OpenStreetMap
The Missing Maps volunteers create map data in OpenStreetMap. They map building footprints or trace roads. Local Missing Maps volunteers help to add further details such as village names, evacuation centers and health facilities. During the days at the Global Platform many stakeholders from national disaster risk reduction agencies and NGOs learned about OpenStreetMap and how valuable a global and open data base of geo-information can be. In many of those countries local OpenStreetMap communities already exist and can function as a focal point for new DRR approaches. We can’t emphasize it enough: Open and free map data created by the OSM community will help to meet the targets of the Sendai Framework.

The potential of Missing Maps
Missing Maps has been around for almost five years but still there are so many places which lack sufficient map data. This is no reason to give up, but to scale up our approach even more. We need consortium such as Missing Maps to foster the communication and exchange between humanitarian organisations, the OpenStreetMap communities and research institutions. Melanie and Rebecca were presenting on this topic on the Ignite Stage at the Global Platform 2019. The questions we received afterwards and at our booth show that we need the Missing Maps to leave no one behind.

As Missing Maps we should strengthen the existing mapping communities and help those who want to get started. Involving more national organisations and sharing our experiences, tools and work flows are key to the mission of Missing Maps. Vice versa, this will help us to reach the local communities and teach how to create data in OSM and make use of it. As Missing Maps is growing, so is its potential impact.

OpenStreetMap based services and tools for Disaster Risk reduction
Creating map data is only the very first step towards better disaster risk planning. OpenStreetMap data extracts on the country level can be downloaded from the Humanitarian Data Exchange platform. This is an easy way the get started with the data. Once data becomes available visualisation and analysis get more important.

At the Heidelberg Institute for Geoinformation Technology, we are working on those tools to make use of the OSM data. The OpenRouteService provides routing functionality based on up-to-date OSM data for several different transportation profiles. For instance, accessibility of health care facilities can provide insights for disaster risk reduction strategies. When using data produced by volunteers, understanding it’s quality is a basic issue. The ohsome platform is designed exactly for this. Looking at how the OSM data has been created and how it changed over time will help us to draw the right conclusions. Missing Maps primary aim is to create map data, but we should think about the analysis directly in the next step.

What’s next?
Disaster Risk Reduction involves many stakeholders with different perspectives. Missing Maps can help to bridge different backgrounds and disciplines. During the Global Platform 2019 we discussed topics such as education, forecast based financing, disaster insurance, inclusion, early warning and many more. Finding concrete examples how those aspects are related to map data will be a topic for Missing Maps in the years to come.

For instance, combining up-to-date data on flood extent or flood forecasts and OSM data and OSM-based services will help to identify vulnerable populations before disasters occur. Another topic which we discussed during the Global Platform 2019 is related to bringing together machine learning and crowdsourcing. The machine learning world is moving fast and new automated approaches to map buildings or land use show huge potential for our work. Missing Maps is addressing this topic already in several projects, e.g. related to the HOT Tasking Manager or MapSwipe. Nevertheless, we are still learning how to integrate an intelligent assistance for mappers into the existing mapping approaches.

The future mission for Missing Maps is clear: Growing the communities and building the tools to fill the blank spots on the global OpenStreetMap.

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