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Last Friday Amin Mobasheri of the GIScience Research Group Heidelberg University successfully defended his PhD in Geoinformatics. Due to the Corona pandemy the defense was conducted in hybrid mode with the committee members participating virtually and only Prof. Zipf and Amin being physically in the room. What a shame there was no joint celebration possible afterwards…
The thesis was related to work done in the EU H2020 CAP4Access project and dealt with methods for analysing and improving OpenStreetMap data quality for routing services for mobility impaired persons.
This is motivated by earlier and still ongoing work to provide navigation services for wheelchair users based on OpenStreetMap data through our routing service openrouteservice.org

Well done and all the best to Amin!

Further related work:

The Sketch Map Tool supports participatory mapping approaches and risk communication. An new paper presents the different functionalities of the tool. OSM data quality can be evaluated regarding the fitness for the sketch-map-approach. Based on the great idea of Field Papers, paper maps can be printed out for the use in the field, and afterwards, the marked sketch maps can be uploaded. They are georeferenced automatically. In addition to the Field Papers homepage, the Sketch Map Tool makes it possible to print the maps in more formats. Thus, if you would like to do a group mapping event, you can print the maps in DIN A0.

Since the tool will be open-source and several analyses are made automatically, the tool also offers a method for local governments in areas where historic data or financial means for flood mitigation are limited. Example analyses for two cities in Brazil show the functionalities of the tool and allow the evaluation of its applicability. Results depict that the fitness-for-purpose analysis of the OpenStreetMap data reveals promising results to identify whether the sketch map approach can be used in a certain area or if citizens might have problems with marking their flood experiences. In this way, an intrinsic quality analysis is incorporated into a participatory mapping approach. Additionally, different paper formats offered for printing enable not only individual mapping but also group mapping. Future work will focus on advancing the automation of all steps of the tool to allow members of local governments without specific technical knowledge to apply the Sketch Map Tool for their own study areas

Klonner, C., Hartmann, M., Dischl, R., Djami, L., Anderson, L., Raifer, M., Lima-Silva, F., Castro Degrossi, L., Zipf, A., Porto de Albuquerque, J. (2021): The Sketch Map Tool Facilitates the Assessment of OpenStreetMap Data for Participatory Mapping. ISPRS Int. J. Geo-Inf., 10, 130. https://doi.org/10.3390/ijgi10030130.



Related work:

Klonner, C., Hartmann, M., Djami, L., Zipf, A. (2019). “Ohsome” OpenStreetMap Data Evaluation: Fitness of Field Papers for Participatory Mapping In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). Proceedings of the Academic Track at the State of the Map 2019, 35-36. Heidelberg.

Klonner, C., Usón, T.J., Aeschbach, N., Höfle, B. (2021): Participatory Mapping and Visualization of Local Knowledge: An Example from Eberbach, Germany. Int J Disaster Risk Sci 12, 56–71. https://doi.org/10.1007/s13753-020-00312-8.

Klonner, C., Hartmann, M., Djami, L., Zipf, A. (2019). “Ohsome” OpenStreetMap Data Evaluation: Fitness of Field Papers for Participatory Mapping. In: Proceedings of the Academic Track at the State of the Map 2019. Heidelberg, Germany, pp. 35-36.

Lightning talk: OpenStreetMap Sketch Map Tool - The Future of OpenStreetMap Field Papers

https://www.geog.uni-heidelberg.de/gis/waterproofing.html

Funded by Belmont Forum and NORFACE joint programme Transformations to Sustainability (T2S) and co-funded by DLR/BMBF (Federal Ministry of Education and Research) as part of its Social-Ecological Research funding priority.

On 1st March 2021, the Swiss Federal Office of Topography (SwissTopo) released all official geodata according to the principles of “Open Government Data (OGD)”. With this step, Swisstopo is making large volumes of high quality geodata freely accessible. We are most excited about the 3D data, which includes digital surface models, digital terrain models and classified point clouds!

Digital terrain model coloured by elevation (left), real point cloud coloured by classification (middle), simulated point cloud coloured by intensity (right).

Digital terrain model coloured by elevation (left), real point cloud coloured by classification (middle), simulated point cloud coloured by intensity (right)

As we believe that open data is a key element to promote more transparent, inclusive and effective research,  we thought to ourselves: Let’s combine SwissTopo open data and our own open software HELIOS++. HELIOS++ is a general-purpose software package for simulation of laser scanning campaigns, developed and maintained by the 3DGeo Research Group.

We loaded a DTM-tile of Säntis (2,502 m a.s.l.) to HELIOS++ and conducted a virtual airborne laser scanning campaign.

Virtual airborne laser scanning of Swiss mountain

Virtual airborne laser scanning of Swiss mountain

Although the exact acquisition settings of the Swisstopo airborne laser scanning campaign are not known, we can configure a simulation that comes reasonably close to the real acquisition:

Comparison of the distribution of points on the terrain between the real point cloud (left) and the simulated point cloud (right)

Comparison of the distribution of points on the terrain between the real point cloud (left) and the simulated point cloud (right)

Such data can be used to complement real data in many use cases. Some examples include the generation of training data for machine learning, especially deep learning, for the validation of algorithms working on point clouds, for acquisition parameter surveys, or for experimentation with novel sensors (see our previous blog post).

Download the Säntis survey here (until 1st April 2021) and reproduce it with the command:

run\helios data\surveys\demo\swisstopo_demo.xml –lasOutput

Documentation of HELIOS++ can be found in the GitHub wiki. Our paper on HELIOS++ is available as a preprint on arXiv:

Winiwarter, L., Esmorís Pena, A., Weiser, H., Anders, K., Martínez Sanchez, J., Searle, M., Höfle, B. (2021): Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic 3D laser scanning. arXiv:2101.09154[cs.CV]

For more information, visit the HELIOS++ project website: https://uni-heidelberg.de/helios

HELIOS++ is funded in part by the BMBF in the frame of the LOKI project (funding code: 03G0890) and by the DFG in the frame of the SYSSIFOSS project (project number: 411263134).

Determining the location of a building’s entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, in a recently published paper in IJGIS we propose a more general approach for inferring the main entrances of public buildings based on the association between spatial elements extracted from OpenStreetMap.
In particular, we adopt three binary classification approaches, weighted random forest, balanced random forest, and smooth-boost to model the association relationship. There are two types of features considered in the classification: intrinsic features derived from building footprints and extrinsic features derived from spatial contexts, such as roads, green spaces, bicycle parking areas, and neighboring buildings. We conducted extensive experiments on 320 public buildings with an average perimeter of 350 m. The experimental results showed that the locations of building entrances estimated by the weighted random forest and balanced random forest models have a mean linear distance error of 21 m and a mean path distance error of 22 m, ruling out 90% of the incorrect locations of the main entrance of buildings. Further details can be found in https://www.tandfonline.com/doi/abs/10.1080/13658816.2020.1861282?journalCode=tgis20

Hu, X., Noskov, A., Fan, H., Novack, T., Gu, F., Li, H., Shang, J.: Tagging the Buildings’ Main Entrance based on OpenStreetMap and Binary Imbalanced Learning. International Journal of Geographical Information Science, DOI: 10.1080/13658816.2020.18

Related Publications:

Hu, X., Fan, H., Noskov, A., Wang, Z., Zipf, A., Gu, F., Shang, J. (2020). Room Semantics Inference Using Random Forest and Relational Graph Convolutional Network: A Case Study of Research Building. Transactions in GIS. https://doi.org/10.1111/tgis.12664

Hu, X., Ding, L., Shang, J., Fan, H., Novack, T., Noskov, A., Zipf, A. (2019). A Data- driven Approach to Learning Saliency Model of Indoor Landmarks by Using Genetic Programming. International Journal of Digital Earth. https://doi.org/10.1080/17538947.2019.1701109

Selected earlier work:

Goetz, M. & Zipf, A. (2013): The Evolution of Geo-Crowdsourcing: Bringing Volunteered Geographic Information to the Third Dimension. In: Sui, D.Z., Elwood, S. & Goodchild, M.F. (eds.): Crowdsourcing Geographic Knowledge. Volunteered Geographic Information (VGI) in Theory and Practice. Berlin: Springer. 2013, XII, 396 pp. 139-159.

Goetz, M. & Zipf, A. (2012): OpenStreetMap in 3D – Detailed Insights on the Current Situation in Germany. AGILE 2012. Avignon, France.

Chen, J & Zipf, A. (2017): DeepVGI: Deep learning with volunteered geographic information. Proceedings of the 26th International Conference on World Wide Web Compagnon. Pages 771-772. https://doi.org/10.1145/3041021.3054250

The research of the 3DGeo group is featured in a press release about Understanding the Spatial and Temporal Dimensions of Landscape Dynamics. The text illustrates how Heidelberg geoinformation scientists develop new computer-based method to analyse topographic changes.

The described method is published in this article:

Anders, K., Winiwarter, L., Mara, H., Lindenbergh, R., Vos, S. E., Höfle B. (2021). Fully automatic spatiotemporal segmentation of 3D LiDAR time series for the extraction of natural surface changes. ISPRS Journal of Photogrammetry and Remote Sensing, 173, pp. 297-308. DOI: 10.1016/j.isprsjprs.2021.01.015.

Free article access is available for one more month (until 30th March 202)1 via this link: https://authors.elsevier.com/a/1cYWO3I9×1fKmO

And if you like the StopMotion visualization, consider watching the full video: https://youtu.be/Fdwq-Cp0mFY

The research is carried out in cooperation with researchers from TU Delft in the frame of the CoastScan project. The PhD project Auto3Dscapes is supported in part by the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp).

HeiGIT wants to serve you better. Therefore we are conducting user feedback surveys regarding our various services.

The Deadline has been extended to March 05rd! Take your chance!

If you have ever used one of our OpenStreetMap based Online Services (or will do so now) for whatever purpose, we’d be very happy if you took the time and filled out the respective survey.

THANK YOU! We will listen to your suggestions.

Find and test the services at HeiGIT.org We appreciate your feedback!

Welcome back to another #ohsome blog post written by our awesome student assistent Sarah! This time we will look at the completeness of railway network data of one specific city in OpenStreetMap, as well as its development. For this we looked at the city of Prague and its completeness of the operator tag. Furthermore, you’ll get to see the development of the railway network data of Prague in an animation (and can even learn how to make one yourself!). In case you haven’t read the last ohsome region of the month blogposts, you can find part 1 here & part 2 here.

Data:

As usual you will have to think of the boundaries you’re going to set in your analysis. For this you again have to get your hands on a spatial data set with the boundaries of Prague (e.g. from here) in the GeoJSON format. The dataset of interest in regard of our railway network analysis can be accessed by sending a request to the ohsome API.

Requests:

For the visualization of the evolution we decided to use the operator tag as indicator, so we can display the ratio of railway network with that information given, as well as the point in time where this information startet to get added and the point in time when it reached its maximum value. We created a snippet with the final cURL POST requests, as well as the parameter text files and further information here.

You will have to use two endpoints for getting the needed data. One is /elements/length/ratio for the part where you want to look at the ratio development over the years and the other one is /elementsFullHistory/geometry so you can access and visualize the whole evolution of railway network data (as given in the filter). With this data extraction request you’ll get all the changes to the railway network within your given timeframe, as well as the duration of validity of these changes, which comes in handy when working on the evolution animation.

Analytical Visualization:

endpoint: /elements/length/ratio

timestamp: 2009-01-01/2021-01-01/P1M

filter: type:way and railway in (rail,light_rail,subway,tram,narrow_gauge) and operator=*

Evolution Visualization:

endpoint: /elementsFullHistory/geometry

timestamp: 2009-01-01,2021-01-01

filter=type:way and railway in (rail,light_rail,subway,tram,narrow_gauge)

Here is the evolution of the railway network of the city of Prague:

As you can see there are two different colors in use. The blue lines symbolize the part of the railway network that does not carry any operator information and the yellow lines represent the part of the network that does have said information added. You might notice the slight “blinking” effect of some of the lines throughout the duration of the animation, which indicates that these lines got edited. For creating this visualization of the evolution you can use the QGIS native Temporal Controller. A short tutorial as well as an introduction to cosmetic options can be found in an additional snippet.

Data Exploration:

Below you can see the ratio development of the the operator tag in the City of Prague. The higher the value the better the covering of the railway network with this information, the highest possible value being 1 (so 100%):

Although the ratio values increase over the years they barely reach 25%. When looking at the datasets we got from our requests, the part of the railway network which actually bares the information of the operator tag seems rather „up-to-date“ as even the name change of the Správa železnic in January 2020 was implemented rather quickly after coming into effect. Yet some of the railway network does not bare the information of an operator, although they most likely belong with one of the two main operators that were named in the dataset, namely Správa železnic & Dopravní podnik hlavního města Prahy, e.g. parts of the metro network do not have the operator tag. The exact reason for that appears to be unclear.

There is a whole list given when looking at the source tag in the full-history dataset, with a lot of them appearing to be linked to the Czech Office for Surveying, Mapping and Cadastre (ČÚZK for short) who offers quite a bit of GIS data. Interestingly enough the operator count wasn’t really used until January 1st, 2012. Throughout the years the overall trend of the ratio values is positive with a few data jumps. Since October 1st of 2016 the ČÚZK has been modifying and updating the INSPIRE-dataset which also happened in connection to their participation of the European Location Framework (ELF) project. The availability of the data might be related for the better ratio values by the end of the given timeframe.

Below you can see the output dataset of the full-history extraction with the Správa železnic operator data highlighted in magenta and the Dopravní podnik hlavního města Prahy operator data highlighted in yellow. The rest of the the railway network remains without an operator tag:

Interestingly enough most of the Metro Network (yellow highlighted lines) appears to be tagged with the operator information when looking at the picture. So at least the subway of Prague appears to have that tag added to it through the years. The “operator-less” part of the railway network however appears to be most of the cities tram network and only some parts of the railway=rail are tagged with operator information (highlighted in magenta).

Even though the ratio values itself are quite low, there is a lot of overall railway data given, especially at the beginning of the timeframe. When looking at the sources, it appears like there has been the opportunity to import data from e.g. orthophotos and datasets given by the Ústav pro hospodářskou úpravu lesů Brandýs nad Labem (ÚHÚL for short), so the Czech Forest Management Institute, or the ČÚZK. Furthermore, the source given for quite some data was Bing. So these input opportunities appear to be the reason why there is quite a lot data given from the start, but when taking the operator tag as our indicator of completeness into consideration, a great part of it appears to be incomplete for some reason. Note: the source=uhul:ortofoto is not being used anymore (since ~Summer 2015) but still had an impact on the dataset in the beginning of the timeframe looked at.

Conclusion:

At last, our region could ideally teach you how to animate a map yourself and has shown you an approach to a completeness analysis with a certain tag. Although the overall ratio values of the city of Prague are still quite small, the local mapping community appears to be rather motivated and active, so one can assume that there is a good chance for an operator tagged future for Prague.

Thank you for reading this months blogpost and stay tuned for there is more to come! As always, you can reach out to us via our email address ohsome(at)heigit(dot)org.

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:

Since 2010 organized humanitarian mapping has evolved as a constant and growing element of the global OpenStreetMap (OSM) community. With more than 7,000 projects in 150 countries humanitarian mapping has become a global community effort. Due to this large amount of projects, it can be difficult to get an overview on mapping activity. This is why we worked on the “Humanitarian OSM Stats” website to make it easier to find the information you are looking for. It combines data from the open-source Tasking Manager hosted by the Humanitarian OpenStreetMap Team (HOT) and information from OpenStreetMap (OSM) that has been processed using the ohsome OSM History Data Analytics Platform developed by HeiGIT.

Let us take for example Médecins Sans Frontières (MSF), which is among the biggest institutional users of the Tasking Manager. Since 2016, MSF has created mapping campaigns both in response to emergency situations, such as the epidemic outbreaks in the Democratic Republic of Congo and cyclones in Mozambique, but also for pre-emptive mapping ahead of a possible programme development.

Goals

In this blog post we will pursue three relatively easy goals:

  • Find out where mapping projects are located.
  • Find out about specific regions on which mapping has been concentrated.
  • Download all the data needed for the things above.

How to get the information from the website

  1. Visit the humstats.org website and select your organization and click on “go”. This will direct you to a new site with the statistics for the selected organization.
  2. A very quick overview on the locations of projects can be obtained from the “Tasking Manager” section. For MSF, we learn that, in total, more than 450 projects have been organized through the Tasking Manager. We further see that mapping has been organized in 26 countries.
  3. More details about the mapping in each country is provided in the map below. The color represents the number of projects in a specific country (darker → more projects). You can hover with the mouse over the map and get more insights. For instance, MSF has organized 40 projects in Nigeria through which more than 850,000 buildings have been added to OSM. More than 8,500 volunteers contributed to this effort. Wow! MSF’s mapping projects have also contributed many roads and buildings in the Democratic Republic of Congo, Central African Republic and Chad to the map. In each of these countries, volunteers have added more than 300,000 buildings to OSM.
Step 1: Select your organization.
Step 2: Check overall Tasking Manager stats.
Step 3: Check countries on the map for number of project and contributors in the Tasking Manager and OSM statistics.

Download the data as a geojson file

If you are interested to get the data behind these numbers and plots continue reading. On the website we offer a list of files to download. The stats_per_country.geojson is the ones that you need for the purpose described in this blog post. For instance, for MSF, this file will be located here: https://humstats.heigit.org/api/export/msf/stats_per_country.geojson.

To be continued

This is the third blog post of a series of posts we are currently working on. If you are interested please reach out to us (benjamin.herfort@heigit.org) and we can try to cover your questions in a future post.

In the next blog post of this series we will take an even closer look at the MSF’s mapping activity in the Democratic Republic of Congo (DRC), Central African Republic (CAR) and Venezuela.

ATTENTION!! One week deadline extension. Are you working on GIS for disaster management? Hurry up! You have until Feb, 21 to submit your WIP or Practitioner paper to GIS Track.

Extended Submission deadline for WiP and Practitioner papers: February 21, 2021 - updated

Track: Geospatial Technologies and Geographic Information Science for Crisis Management (GIS)
https://www.drrm.fralinlifesci.vt.edu/iscram2021/files/CFP/ISCRAM2021-Track10-Geospatial_Technologies

https://www.drrm.fralinlifesci.vt.edu/iscram2021/call-papers.php

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

In acknowledgement of the ongoing challenges posed by the COVID-19 pandemic, the original submission deadlines for ISCRAM 2021 have been extended by several weeks.

The 2021 ISCRAM conference invites three types of paper submissions:

  • Deadline passed: CoRe - Completed Research (from 4000 to 8000 words).
  • WiP - Work In Progress (from 3000 to 6000 words).
  • Practitioner (from 500 to 3000 words).

HeiGIT wants to serve you even better. Therefore we are conducting user feedback surveys regarding our various services.
If you have ever used one of our OpenStreetMap based Online Services (or will do so now) for whatever purpose, we’d be very happy if you took the time and filled out the respective survey.

THANK YOU! We will listen to your suggestions.

Find and test the services at HeiGIT.org We appreciate your feedback!

HeiGIT.org

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