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Multi-sensor remote sensing image classification has been considerably improved by deep learning feature extraction and classification networks. In this recent paper, we propose a novel multi-sensor fusion framework (CResNet-AUX) for the fusion of diverse remote sensing data sources. The novelty of this paper is grounded in three important design innovations:

  • A unique adaptation of the coupled residual networks to address multi-sensor data classification;
  • smart auxiliary training via adjusting the loss function to address classifications with limited samples;
  • A unique design of the residual blocks to reduce the computational complexity while preserving the discriminative characteristics of multi-sensor features.

The proposed classification framework is evaluated using three different remote sensing datasets: the urban Houston university datasets (including Houston 2013 and the training portion of Houston 2018) and the rural Trento dataset. The proposed framework achieves high overall accuracies of 93.57%, 81.20%, and 98.81% on Houston 2013, the training portion of Houston 2018, and Trento datasets, respectively. Additionally, the experimental results demonstrate considerable improvements in classification accuracies compared with the existing state-of-the-art methods.

More importantly, the proposed CResNet-AUX is designed to be a fully automatic generalized multi-sensor fusion framework, where the network architecture is largely independent from the input data types and not limited to specific sensor systems. Our framework is applicable to a wide range of multi-sensor datasets in an end-to-end, wall-to-wall manner.

Future works in developing intelligent and robust multi-sensor fusion methods may benefit from the insights we have produced in this paper. In further research, we propose to test the performance of our framework on a large-scale application (e.g., continental and/or planetary land use land cover classification) and include additional types of remote sensing data. Find more details in the paper:

Li, H.; Ghamisi, P.; Rasti, B.; Wu, Z.; Shapiro, A.; Schultz, M.; Zipf, A. (2020) A Multi-Sensor Fusion Framework Based on Coupled Residual Convolutional Neural Networks. Remote Sensing. 12, 2067. https://doi.org/10.3390/rs12122067

What does the tool behind the recently published documentation of the ohsome API have in common with a statue made of stones in Egypt? - Apart from the name, both are of course awesome (and now also ohsome). As previously announced, together with the open source release 1.0 of the ohsome API, we have published a new set of documentation pages. We are giving you now a much more detailled insight in how to properly use the different endpoints and parameters of our API, as well as showing concrete examples for different endpoints. The Swagger documentation, which used to be our prime source for information of the API is still available and can be used as a playground to test simple GET requests. Let’s take a look at the different sections of the new documentation.

The main part of the docs lists the different endpoints, namely the general aggregation, user aggregation, extraction and metadata endpoints. All of them have a short description, followed by the possible aggregation, or geometry types, as well as a specific set of valid parameters. You can find further example requests for every endpoint in four different formats: GET, POST, and the two programming languages Python and R. This should help you to get a better idea on how to access our API using one of these languages. Please also note the various “! Note” windows giving additional information to the respective endpoint.

The second part displays two visualizations of the API endpoints and shows the hierarchy behind them (you can see one of the two graphs just here below). The first ones gives a graphical view on the various endpoints and the second one a rather textual one using indenting as an identifier of the hierarchy within the endpoints. Both are providing a different perspective on the same schema and summon up to a total number of 68 endpoints.

The third part of the docs can be found under the chapter “Additional Information” and comprises, as the name might suggest, of additional infos on the grouping endpoints, as well as the time, filter and spatial boundary (bboxes, bpoints, bpolys) parameters. It gives an in-depth explanation on the different posibilities that these parameters and endpoints offer and how their specific formats look like. The filter parameter is a special case, as it has just recently been introduced to the ohsome API with version 1.0 and will be presented in a follow-up post coming next week. If you want to know more about the ohsome API, or any other component of the ohsome platform, just contact us via email info@heigit.org.

No place for climate change: A recent transdisciplinary study on the viability of public spaces in cities under increasing heat was featured in the official Heidelberg University Newsroom (English version here).

The study has been conducted by a Heidelberg research team led by Dr. Kathrin Foshag and investigated the heat stress in selected urban squares in Heidelberg and identified adaptation measures that can be taken.

Find more details about the study in a recent blogpost and in the full publication:

Foshag, K., Aeschbach, N., Höfle, B., Winkler, R., Siegmund, A., Aeschbach, W., 2020: Viability of public spaces in cities under increasing heat: A transdisciplinary approach, Sustainable Cities and Society, Volume 59, August 2020, 102215, https://doi.org/10.1016/j.scs.2020.102215

The Humanitarian OpenStreetMap Team (HOT) has announced major financial support from the Audacious Project, which will be provided over the next five years. HOT aims to use this funding to grow OpenStreetMap (OSM) communities in 94 countries. By engaging one million volunteers the goal is to map an area home to one billion people living in poverty and at high risk of disaster.

Congratulations! This is not only a big step for HOT as an organization but also an award for the humanitarian and general OSM community.

At GIScience Research Group and HeiGIT, we’ve accompanied HOT’s way over the past decade. Starting with the response to the Haiti earthquake in 2010 and the mapping efforts which followed after, the Fukushima Tsunami in 2011, Typhoon Haiyan in 2013 or the Nepal Earthquakes in 2015, to name a few, we’ve seen how valuable HOT’s mission is towards better decisions in natural and humanitarian disasters. We’ve also taken part in HOT’s journey towards disaster risk reduction and pre-emptive mapping of vulnerable regions through our common support of the Missing Maps project. The research we’ve conducted on the Missing Maps project for last year’s State of the Map conference shows how HOT and the other Missing Maps members have scaled the remote mapping in OSM. The Audacious Project is the next destination on that journey.

HOT’s audacious vision comes with four goals:

First, we will grow our global volunteer contributor community by adding regional “hubs” to the existing network of OpenStreetMap communities in each region. Hubs will act as connectors to facilitate exchange of ideas and expertise across countries and provide financial and technical support to OSM communities in 94 countries (20-25 per region).

Second, we’ll make significant investments in technology that enhances mapping contributions on mobile to enable scaling of local contributions to OpenStreetMap.

Third, we’ll invest in map data quality and ethical collection and use of map data.

Fourth, we’ll ensure the data we create has a tangible impact by expanding our national and regional partnerships with humanitarian organizations, governments, and other actors to help them use OpenStreetMap to deliver more effective and efficient aid.

It’s encouraging to see that this vision is shared by the Audacious Project. It underpins what we’ve been working on in Heidelberg for the last couple of years as well. Especially when it comes to OSM data quality, we’ve built the basis to analyze the growth of OSM and its community in almost unlimited spatial and temporal dimensions. HeiGIT and GIScience offer a growing set of open source tools and services that support humanitarian aid and we are curious to see how we can further support through our research and development.

Examples include work in the context of the Missing Maps initiative, such as conceptualising and extending microtasking apps like the award-winning MapSwipe, the combination with AI to improve its effectiveness, as well as services to analyse MapSwipe data and making it usable through a workflow integration in the HOT Tasking Manager. The data is also used in deep learning applications for generating settlement layers and feedback for microtasked mapping or for finding unmapped areas in OSM. Improving and analysing the data quality of OSM as a major data set for humanitarian aid is supported through the growing ohsome framework (OpenStreetMap History Analytics platform), including an API for related analysis (e.g. also: Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring or Analyzing the spatio-temporal patterns and impacts of large-scale data production events in OpenStreetMap), and map dashboards like the first versions of the OSM History Explorer, that already includes some selected map layers relevant for HOT, such as WASH-related OSM tags.

Of course, the openrouteservice for disaster management combined with realtimeOSM already offers OSM logistics and routing solutions within disasters based on the latest OSM data, but needs enhancements or modifications according to the needs of the real-world users and further coverage for relevant world regions. Similarly, the ORS isochrones for accessibility analysis are a relevant tool for project and disaster management and humanitarian contexts (e.g. analysing the healthcare system in Africa).

Apart from the tools and services that support remote contributions, analyses, planning and coordination as well as monitoring of open geodata, GIScience is also increasingly supporting local mapping approaches. These are fostered through our work within the Missing Maps initiatve as well as in the Waterproofing data project. In the scope of the Waterproofing data project, an extension of FieldPaper tools, which are commonly used for local data collection, has been developed which bundles several intrinsic analyses to evaluate a study region’s fitness for usage and therefore facilitates quality-aware participatory mapping in the field.

Furthermore, the Audacious Project is another accelerator towards the further assessment of automated mapping. Examples for our first approaches with HOT include the assessment of AI-assisted mapping in comparison to traditional mapping in which we build on previous research in our group (e.g.  Exploration of OpenStreetMap Missing Built-up Areas using Twitter Hierarchical Clustering and Deep Learning in Mozambique, Estimating OpenStreetMap Missing Built-up Areas using Pre-trained Deep Neural Networks, Mapping Human Settlements with Higher Accuracy and Less Volunteer Efforts by Combining Crowdsourcing and Deep LearningDeep Learning from Multiple Crowds: A Case Study of Humanitarian Mapping etc.). Doing this not only in an efficient but also high quality and ethical way poses challenges for the entire OSM community. We believe that this is a field where good research is the needed basis for an acceptable solution.

And last but not least we support capacity building with many workshops, conferences and mapathons or research projects and related activities. Further information can be found in our publications in journals and conferences (e.g. about Identifying Elements at Risk from OpenStreetMap: The Case of Flooding to A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information or Open land cover from OpenStreetMap and remote sensing….).

We are looking forward to cooperate with relevant stakeholders to further advance GI tools and open geoinformation for humanitarian aid and disaster risk reduction.

There are exciting times ahead. Let’s use this great opportunity to strengthen the entire OSM ecosystem and bring digital humanitarian aid to the next level.

Overview:

Scholz, S., Knight, P., Eckle, M., Marx, S., Zipf, A. (2018): Volunteered Geographic Information for Disaster Risk Reduction: The Missing Maps Approach and Its Potential within the Red Cross and Red Crescent Movement. Remote Sensing, 10(8), 1239, doi: 10.3390/rs10081239.

[tl;dr]

Using hospital locations and the ORS Isochrone service, we have created a method for comparing physical access to healthcare in Sub-Saharan Africa. Hospital locations were derived from OpenStreetMap and compared against another free available dataset. Results indicate strong similarity in both hospital datasets, however the uncertainty of our method requires further evaluation.

Figure 1 Stacked barchart comparing population proportions reached within 1 hour and within 10 minute intervals for OSM versus KEMRI. (HeiGIT 2020)
Figure 1 Stacked barchart comparing population proportions reached within 1 hour and within 10 minute intervals for OSM versus KEMRI. (HeiGIT 2020)

Motivation
Since the beginning of the COVID-19 pandemic we have seen the virus traveling around the world. Starting from Wuhan, China in December 2019, Europe became epicenter of the crisis in March 2020 and was followed by the U.S. the next month. Just recently, WHO declared Latin America the new epicenter, with cases on the rise especially in Brazil. Africa, a continent of ~1.27 billion people and only ~2000 ventilators received little media coverage until now. The pandemic reached the continent in late February and spread to every country until mid May. Today there are ~315,000 confirmed cases and ~8,300 deaths reported in Africa. Officially no African country suffered a major outbreak like northern Italy or New York City. But chances are an outbreak could occur undetected for a while. In the global race for virus gear, African countries lack the economic power to compete for medical resources for response and testing efforts (Kavanagh et al. 2020). Therefore the structure of health systems will presumably play a key role in coping with the pandemic.

Method
We approached the question about the structure of healthcare systems in Sub-Saharan African (SSA) using a proxy - physical accessibility of hospitals. Using publicly available hospital location data, the openrouteservice ORS Isochrone service and population data, we were able to calculate travel-time distances from hospital locations and the respective reached population per country in SSA. In a previous blogpost we compared the completeness of health facilities mapped in OSM with a repository of public health facilities assembled by a team of the KEMRI-Wellcome Trust Research Programme (Maina et al. 2019). For the analysis in this blog, we used both OSM and KEMRI facilities. For each country we requested 1hour travel time isochrones using a car-driving profile. Afterwards, we merged the isochrones and extracted population information from a 1km raster grid by WorldPop.

Figure 2 Scatterplot of population proportions reached within 1 hour using either hospitals mapped in OSM or KEMRI. Pearson. (HeiGIT 2020)

Figure 2 Scatterplot of population proportions reached within 1 hour using either hospitals mapped in OSM or KEMRI. Pearson. (HeiGIT 2020)

Findings
Although both datasets differ significantly in the number of hospitals represented, we found that disparities are rather marginal when used for an accessibility analysis (figure 1). The part of the population within an hour driving distance is 49.8 % when using hospitals mapped in OSM and 47.9% when relying on hospitals in KEMRI. The country with the least reached proportion of inhabitants for both data sources is South Sudan (OSM: 6.99%; KEMRI: 8.81). The best performing country for OSM is Burundi, for KEMRI it is Rwanda. KEMRI offers 4,831 hospitals for SSA, whereas in OSM with 13,460 almost three times as much hospitals are provided. The mean difference in reached population is 5% and ranges from 0.06% in Ethiopia to 14.9 % in the Gambia. Overall, there is a strong correlation of  population proportion reached by both data sources indicated by a Pearson r of 0.973 (figure 2).

Figure 3 Maps on distribution of population, time-distance from hospitals and hospital locations for OSM and KEMRI. (HeiGIT 2020)

Figure 3 Maps on distribution of population, time-distance from hospitals and hospital locations for OSM and KEMRI. (HeiGIT 2020)

When we look at the differences at the country level we see that effects of over- or underestimation of hospitals is not crucial for our analysis. Figure 3 shows the distribution of population, hospitals and reached area within 1 hour for Nigeria and Lesotho. Nigeria is represented with 2,907 hospitals in OSM and 887 in KEMRI. Lesotho with 56 in OSM and 14 in KEMRI. Regardless of OSM overestimating the amount of hospitals by far, reached proportion of population for Nigeria differs only about 11.9% and for Lesotho 1.3%. Although disagreeing on the amount of hospital facilities, OSM follows a similar distribution like KEMRI and vice versa.

But how do patterns of facility distribution like we see for Nigeria emerge? The central north is super densely covered by hospitals. A bit of research revealed data imports in the region. In Kano and Bauchi, two states in the very same location were subject to an import of ~1,500 facilities in 2014. For Borno state, located north east ~ 500 facilities were imported in 2015.

Limitations & Outlook
Further research on the reliability of the results is needed. The accuracy of both KEMRI and OSM is questionable and requires robust assessment. The underlying model of the isochrone service uses the OSM road network. Completeness and accuracy of OSM can vary across regions. Additionally the isochrone model is built to cover a global scale, therefore the time distance estimates must be treated with caution, especially in regional contexts where we assume travel speed to differ from estimates in more industrialized settings. Tools and services like the OpenStreetMap History Analytics platform ohsome and ohsomeHEX will help us to better understand OSM data and its evolution in this and other contexts. Stay tuned as we will dig deeper into the question on how meaningful our results are in a coming blog post.

As we’ve announced it in a previous post, the Open Source release 1.0 of the ohsome API has finally arrived. As a reminder, or for those of you that hear “ohsome” for the first time, 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 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.

This step of making the first major release of the ohsome API marks the achievement of a thoroughly planned and long awaited milestone and brings a bunch of fixes, together with new features. As this would be too much to describe in one blog post, we will publish further posts in the next days explaining the new filter parameter, as well as the enlarged documentation of the API. If you’re already curious and want to try it out yourself, check out the links behind the aforementioned features, so you could directly go for it and fire requests onto our global 1.0 instance using this new knowledge. Stay tuned for more upcoming ohsome blog posts!

p.s.: To give you some ideas, here is a selection of past blog posts that show potential use cases of our API:

ohsome logo
Related References:

Raifer, M, Troilo, R, Kowatsch, F, Auer, M, Loos, L, Marx, S, Przybill, K, Fendrich, S, Mocnik, FB & Zipf, A (2019): OSHDB: a framework for spatio-temporal analysis of OpenStreetMap history data. Open Geospatial Data, Software and Standards, https://doi.org/10.1186/s40965-019-0061-3.

Auer, M.; Eckle, M.; Fendrich, S.; Griesbaum, L.; Kowatsch, F.; Marx, S.; Raifer, M.; Schott, M.; Troilo, R.; Zipf, A. (2018): Towards Using the Potential of OpenStreetMap History for Disaster Activation Monitoring. ISCRAM 2018. Rochester. NY.

Grinberger, A. Y. ; Schott, M. ; Raifer, M ; Troilo, R. ; Zipf, A. (2019): The institutional contexts of volunteered geographic information production: a quantitative exploration of OpenStreetMap data. Proceedings of the GeoCultGIS - Geographic and Cultural Aspects of Geo-Information: Issues and Solutions, Limassol (Cyprus).

Ludwig, C. ; Zipf, A. (2019): Exploring regional differences in the representation of urban green spaces in OpenStreetMap. Proceedings of the GeoCultGIS - Geographic and Cultural Aspects of Geo-Information: Issues and Solutions, Limassol (Cyprus).

Klonner, Hartmann, Djami, 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, Germany

Today the time has come: The “meinGrün” web app for Dresden and Heidelberg is officially launched.

With the mobile application you can (re-)discover known and unknown green spaces and find a pleasant route to those. Users can learn about the functions of the app via virtual scavenger hunt. The app is the result of the meinGrün project. This is funded by the German Federal Ministry of Transport and Digital Infrastructure as part of the Research Initiative Modernity Fund (mFUND).

R. Hecht/IÖR-Media

Photo: R. Hecht/IÖR-Media

Whether a family picnic, soccer with friends, a walk around the dog or a quiet observation of nature - every outdoor activity needs a suitable green space. With the meinGrün app, you can now quickly and easily find the green that best suits your own needs. The mobile application not only provides information about the location of public green spaces, but also provides information about the equipment on site. Where is there a playground, where is a lawn? Which park also offers a barbecue area and where can you browse through your favorite book on a quiet bench? With a variety of search functions, everyone can find the personal optimal green space.

And that’s not all. The meinGrün app also provides information on how to get into the green. New routing options allow you to find not only the shortest route, but also the quietest, greenest, or the route that offers the most shade. This function is based on the openrouteservice, which is developed by the Heidelberg Institute for Geoinformation Technology (HeiGIT) and the GIScience Research Group at Heidelberg University. With the app, we would like to encourage people to make their way into the green as environmentally friendly as possible, ideally on foot or by bike.

From June 19, the meinGrün app will be available to all interested parties in the pilot cities of Heidelberg and Dresden. In order to make it easier to use, the project team has developed several virtual scavenger hunts. A four-kilometer version starts in Dresden on the playground in Park Bürgerweise, a second 13-kilometer route on Dresden’s Albertplatz. The starting point in Heidelberg is the Neckarwiese. The paper chases guide the participants from one green area to the next with the help of the meinGrün app and exciting tasks. All the functions that the app offers can be learned in a playful way. Those who take part in the scavenger hunt also have the opportunity win a prize in a contest!

The meinGrün app https://meingruen.org/

Further info  https://www.geog.uni-heidelberg.de/gis/meingruen_en.html http://meingruen.ioer.info/

References:

Novack, T.; Wang, Z.; Zipf, A. (2018): A System for Generating Customized Pleasant Pedestrian Routes Based on OpenStreetMap Data. Sensors 2018, 18, 3794.

Ludwig, Christina ; Zipf, Alexander (2019): Exploring regional differences in the representation of urban green spaces in OpenStreetMap. Proceedings of the GeoCultGIS - Geographic and Cultural Aspects of Geo-Information: Issues and Solutions, Limassol (Cyprus)

Wang Z., T. Novack, Y. Yan, A. Zipf (2020, accepted): Quiet Route Planning for Pedestrians in Traffic Noise Polluted Environments. IEEE Transactions on Intelligent Transportation Systems.(accepted)

Background:

The meinGrün project is funded by the Federal Ministry of Transport and Digital Infrastructure (BMVI) as part of the mFUND research initiative (FKZ: 19F2073A). The project consortium includes the Leibniz Institute for Ecological Spatial Development (project management), the German Remote Sensing Data Center of the German Aerospace Center (DLR), the Institute of Cartography at the Technical University of Dresden, the Heidelberg Institute for Geoinformation Technology HeiGIT) and GIScience Research Group at Heidelberg University, the Institute for software development and IT consulting in Karlsruhe as well as urbanista in Hamburg and Terra Concordia in Berlin. In developing the meinGrün WebApp, the project team also worked closely with the administrations of the two pilot cities of Dresden and Heidelberg. They not only provided advice, but also provided municipal (green space) data for the project.

In 18 months of scientific work, technical implementation and practical tests, the project partners developed the meinGrün app. The application combines various data, including open geodata and the latest remote sensing data from the European space program Copernicus, as well as user-generated data from OpenStreetMap. The meinGrün WebApp was initially developed for the pilot cities of Dresden and Heidelberg. But it can also be transferred to other cities.

In scope of the disastermappers heidelberg mapathon series “Open Data & the Sustainable Development Goals”, the next virtual OpenStreetMap mapping event “Mapping Human Rights” will take place on Tuesday, June 30th from 6 pm, in cooperation with Amnesty International Heidelberg. Amnesty International Heidelberg will give us insights into Human Rights and the work of Amnesty International. After their presentation, you will be given an overview on the Mapping projects for the evening as well as an easy to follow introduction to OpenStreetMap.

We will then work on two different areas in the Philippines. The first project is suitable for beginners and focuses on areas that are affected by climate change, the second project is around “Early Flood Action” in the Philippines and is aimed at advanced mappers.

Join us and become part of the global mapping community via https://heiconf.uni-heidelberg.de/loh-py6-jxt. Please prepare a headset to communicate, as well as a mouse, which will make mapping more comfortable.

Everyone is welcome and our mapping tutorial ensures an appropriate introduction to all contents. No previous knowledge is required.

Sveriges Television AB (the public Swedish television company) has published a data journalism WebApp showing how far one can travel according to the recommendations from the Swedish Public Health Authority. Of course the safest recommendation is not to travel at all during the corona crises - but if one absolutely has to, shorter trips of up to two hours drive from the place of residence are valid according to recommendations from the Swedish Public Health Authority.

In the WebApp users can click on the map or search for a city and get an animated visualisation about the 2 hour isochrones (travel times) from the center of the postal area. The calculation shows how far one can get within two hours of lawful driving. The data is from OpenStreetMap and the interactive isochrone calculations are made using OpenRouteService.

Keep your distance and happy summer!

https://www.svt.se/datajournalistik/sa-langt-kommer-du-pa-tva-timmar/

Am 19. Juni ist es soweit: Die „meinGrün“-WebApp für Dresden und Heidelberg geht offiziell an den Start. Mit der mobilen Anwendung lassen sich bekannte und unbekannte Grünflächen und der Weg dorthin neu entdecken. Per virtueller Schnitzeljagd können Nutzerinnen und Nutzer die Funktionen der App kennenlernen. Die App ist Ergebnis des Projektes meinGrün. Dieses wird im Rahmen der Forschungsinitiative Modernitätsfonds (mFUND) durch das Bundesministerium für Verkehr und digitale Infrastruktur gefördert.

R. Hecht/IÖR-Media

Foto: R. Hecht/IÖR-Media

Ob Familienpicknick, Fußball mit Freunden, Gassi-Runde mit dem Hund oder stilles Beobachten der Natur – jede Aktivität im Freien braucht eine dafür geeignete Grünfläche. Mit der meinGrün-App lässt sich nun dasjenige Grün schnell und einfach finden, das am besten zu den eigenen Bedürfnissen passt. Denn die mobile Anwendung hält nicht nur Informationen zur Lage öffentlicher Grünflächen bereit, sondern liefert auch Informationen zur Ausstattung vor Ort. Wo gibt es einen Spielplatz, wo eine Liegewiese? Welcher Park bietet auch einen Grillplatz und wo lässt sich auf einer ruhigen Bank im Lieblingsbuch schmökern? Mit einer Vielzahl an Suchfunktionen kann jeder die optimale Grünfläche finden.

Und das ist noch nicht alles. Auch zum Weg ins Grün liefert die meinGrün-App Informationen. Neue Routing-Optionen erlauben es, nicht nur den kürzesten Weg zu finden, sondern auch den leisesten, grünsten oder den Weg, der den meisten Schatten bietet. Diese Funktion basiert auf dem openrouteservice, welcher vom HeiGIT (Heidelberg Institute for Geoinformation Technology) und dem Lehrstuhl für Geoinformatik der Universität Heidelberg entwickelt wird. Mit der App möchte das Projektteam anregen, den Weg ins Grün auch möglichst umweltschonend, am besten zu Fuß oder per Fahrrad zurückzulegen.

Ab 19. Juni steht die meinGrün-App in den Pilotstädten Heidelberg und Dresden allen Interessierten zur Verfügung. Um die Nutzung zu erleichtern, hat das Projektteam mehrere virtuelle Schnitzeljagden entwickelt. Eine vier Kilometer lange Version startet in Dresden auf dem Spielplatz im Park Bürgerweise, eine zweite 13 Kilometer lange Strecke am Dresdner Albertplatz. Der Startpunkt in Heidelberg ist die Neckarwiese. Die Schnitzeljagden lotsen die Teilnehmenden mithilfe der meinGrün-App und spannenden Aufgaben von einer Grünfläche zur nächsten. Spielerisch lassen sich so alle Funktionen, die die App bietet, kennenlernen. Wer sich an der Schnitzeljagd beteiligt, hat zudem die Möglichkeit, an einem Gewinnspiel teilzunehmen.

Der Weg zur meinGrün-App ab dem 19. Juni: https://meingruen.org/

Kontakt im Leibniz-Institut für ökologische Raumentwicklung (IÖR), Dresden: Dr. Robert Hecht und Patrycia Brzoska, E-Mail: meingruen@ioer.de

Kontakt am HeiGIT / Lehrstuhl für Geoinformatik, Universität Heidelberg: Prof. Dr. Alexander Zipf, Dr. Sven Lautenbach, Christina Ludwig

Hintergrund:

Das Projekt meinGrün wird vom Bundesministerium für Verkehr und digitale Infrastruktur (BMVI) im Rahmen der Forschungsinitiative mFUND gefördert (FKZ: 19F2073A). Zum Projektkonsortium gehören das Leibniz-Institut für ökologische Raumentwicklung (Projektleitung), das Deutsche Fernerkundungsdatenzentrum des Deutschen Zentrums für Luft- und Raumfahrt (DLR), das Institut für Kartographie der Technischen Universität Dresden, das Heidelberg Institute for Geoinformation Technology an der Universität Heidelberg, das Institut für Software-Entwicklung und EDV-Beratung in Karlsruhe sowie urbanista in Hamburg und Terra Concordia in Berlin. Bei der Entwicklung der meinGrün-WebApp kooperierte das Projektteam darüber hinaus eng mit den Verwaltungen der beiden Pilotstädte Dresden und Heidelberg. Sie standen nicht nur beratend zur Seite, sondern haben auch kommunale (Grünflächen-)Daten für das Projekt zur Verfügung gestellt.

In 18 Monaten wissenschaftlicher Arbeit, technischer Umsetzung und praktischer Tests haben die Projektpartner die meinGrün-App entwickelt. Die Anwendung kombiniert verschiedene Daten, darunter offene Geodaten und neueste Fernerkundungsdaten aus dem europäischen Raumfahrtprogramm Copernicus, außerdem fließen nutzergenerierte Daten in die Anwendung ein. Die meinGrün-WebApp wurde zunächst für die Pilotstädte Dresden und Heidelberg entwickelt. Sie lässt sich aber auch auf andere Städte übertragen.

Weitere Informationen zum Projekt: http://meingruen.ioer.info/

Link zum Blog: https://meingruen.org/

vgl:

Novack, T.; Wang, Z.; Zipf, A. (2018): A System for Generating Customized Pleasant Pedestrian Routes Based on OpenStreetMap Data. Sensors 2018, 18, 3794.

Ludwig, Christina ; Zipf, Alexander (2019): Exploring regional differences in the representation of urban green spaces in OpenStreetMap. Proceedings of the GeoCultGIS - Geographic and Cultural Aspects of Geo-Information: Issues and Solutions, Limassol (Cyprus)

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