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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.

As promised earlier this year, we are very happy to finally announce the unveiling of our new route optimization endpoint!

We deployed an instance of the popular Vroom open-source engine, which is capable of solving complex Vehicle Routing Problems (VRP) in record time. This type of problem always occurs when multiple locations need to be visited in the optimal order by one or more vehicles. Consequently, it’s most valuable for logistics planning, but is also useful for traveling sales persons (which actually is the name of a particular VRP). With Vroom job and vehicle scheduling is a breeze.

The optimization service supports advanced parameters to constrain the optimization, such as:

  • capacities: each vehicle can have separate capacities for multiple goods, each job will consume a vehicle’s capacity
  • time windows: each vehicle can have a start and end time (e.g. working hours), each job can have multiple time windows, expressed as week seconds, e.g. Mon 8 am = 28800
  • skills: each job can require skills the vehicle must meet
  • service duration: each job can take a specified amount of time
Optimize two vehicles for 6 jobs

Optimize two vehicles for 6 jobs in Berlin

The full documentation how to use this endpoint can be found in our Openrouteservice API documentation or on Vroom’s Github page.
The optimization works with all available profiles (car, various bike variants, pedestrian, wheelchair and more) of OpenRouteService. So far, it has already been implemented in the Python SDK, but will soon be available in the JavaScript, R, and QGIS clients as well.
See also our related research on healthy, quiet and green routingwheelchair accessibility, Landmark navigation or routing through open spaces and more.
Happy optimizing!

The 3DGeo group is currently testing their new UAV-borne LiDAR (ULS) system. The integrated system consists of a DJI Matrice 600 copter, a RIEGL miniVUX-1 UAV laser scanner and is complemented by an inertial measurement unit and a realtime kinematics GNSS. Combining the different components in one system allows for high-accuracy (cm) acquisition of 3D geodata.

The new ULS system enables us to acquire larger areas compared to our terrestrial laser scanning systems and offers higher point densities and positional accuracies compared to airborne laser scanning.
Thus, various ULS campaigns are already planned for different applications in our projects.

Within the SYSSIFOSS project, ULS will be used for the acquisition of high-density point clouds from different tree species in forests. These point clouds will then, for example, be used to validate species-specific model trees derived from airborne laser scanning which will serve as an input for LiDAR simulations in HELIOS.

Moreover, multi-temporal ULS datasets will be acquired at the rock glacier Äußeres Hochebenkar (Ötztal Alps, Austria) between May and September 2019. The data will be used to extend the multi-temporal dataset and the analysis and quantification of rock glacier surface changes. Furthermore, the ULS point clouds will be valuable to develop existing approaches and methods further.

Additionally, members of the 3DGeo group are currently attending a RIEGL training session to gain valuable experience with the new RIEGL miniVUX-1 UAV laser scanner.

We are looking foward to the upcoming missions of our new ULS system.

First ULS test flights in Heidelberg.

First ULS test flights in Heidelberg.

This year, Sabrina Marx and Martin Hilljegerdes were teaching the block course “GIS 2” at the Heidelberg Center for Latin America (HCLA) in Santiago de Chile. The course is part of the international Master program “Governance of Risks and Resources”, offered by Heidelberg University in cooperation with Pontificia Universidad Católica de Chile and Universidad de Chile. The collaborative Master program seeks to provide tools and profound knowledge regarding the management of natural resources and risks for the territorial planning process as well as the governance of related institutions.

During the “GIS 2″ course, conceptual discussions related to Volunteered Geographic Information (VGI), crowdsourcing and implicit knowledge were presented. In addition, tools for collecting and analyzing (user-generated) geoinformation were introduced and applied by the students for mitigation, response and recovery of natural hazards. Theoretical background information as well as the use of such tools during the sessions enabled the students to gain deeper understanding of factors influencing disaster risk management. In a final session with a disaster scenario, the students could apply them for a field campaign.

19th-22nd of May, the 16th Information Systems for Crisis Response and Management (ISCRAM) conference is taking place in Valencia.

Likewise to previous years, the GIScience Research Group/HeiGIT are taking part and supporting with several contributions.

Martin Hilljegerdes will present a paper, based on his Master Thesis, focusing on “Evaluating the effects of consecutive hurricane hits on evacuation patterns in Dominica” in the first “Geospatial Technologies and Geographic Information Science for Crisis Management” session Monday afternoon. This session is chaired by Carolin Klonner.

Carolin Klonner and Melanie Eckle will furthermore present during the poster session on Monday evening. Carolin will herein share current work of the Waterproofing Data project, about “Gathering Local Knowledge for Disaster Risk Reduction: The Use of Sketch Maps in Group Discussions“.

Melanie will likewise present current work that she conducted in the scope of her Master Thesis, around “Towards Bridging the Gap between Demand and Supply in Humanitarian Geodata Use“.

Apart from that, already tomorrow Melanie will present Missing Maps and a small “poster teaser” in the “Workshop on Encouraging Productive Interaction between Practitioners and Researchers“. See the full program here.

We are looking forward to great exchanges and to meeting many of you there!

On 14th and 15th May, our 3DGeo group members Bernhard Höfle and Lukas Winiwarter were co-organizing and participating in the 4th colloquium for PhD students working on the topic of Deep Learning and its applications in Photogrammetry, Remote Sensing and Geoinformation Processing of the Deutsche Geodätische Kommission (DGK) and the Deutsche Gesellschaft für Photogrammetrie und Fernerkundung (DGPF). Participants from institutions all over Germany gathered at the University of Rostock to present their work and discuss current challenges in their PhDs. Intensive Feedback was provided by both the PhD students themselves and by the organizing commitee (Prof. Dr. Bernhard Höfle, Prof. Dr.-Ing. Jan-Henrik Haunert and Prof. Dr.-Ing. Ralf Bill). Additionally, two keynote talks were given by Prof. Dr.-Ing. Franz Rottensteiner and Prof. Dr.-Ing. Ribana Roscher.
Lukas Winiwarter presented his ideas on the application of Deep Learning techniques to single-point based time series analysis methods, one of the core research topics of the 3DGeo group.
The scientific program was complemented by a social dinner in the city center of Rostock, where the students and supervisors got to exchange in a more relaxed environment.

Letzte Woche fand das zweite große Konsortiumstreffen im Projekt „meinGrün“ in Heidelberg statt. Zusammen mit unseren Projektpartnern vom IÖR , DLR , ISB AG , dem Institut für Kartographie der TU Dresden , Terra Concordia (mundraub.org) und Urbanista haben wir zwei Tage lang an der Weiterentwicklung unserer App gearbeitet, welche es Bürgerinnen in Heidelberg und Dresden ermöglichen soll, einfach und unkompliziert die für sie geeignetste Grünfläche zu finden - sei es ein ruhiges Plätzchen mit schattiger Bank zum entspannen oder eine große Wiese zum Fußball spielen.

Während des Treffens gab es regen Austausch zum aktuellen Stand der einzelnen Projektteams und es wurde intensiv an der Weiterentwicklung des Konzepts der App und der zugrundeliegenden Datendienste gearbeitet. Mittwoch Vormittag besuchte uns Christian Scholl von der Abteilung „Fördermittelmanagement und Open Government“ der Stadt Heidelberg. Zusammen haben wir mögliche Nutzergruppen der App und potentielle Partnerorganisationen in Heidelberg identifiziert wie zum Beispiel Sportvereine, Seniorentreffs oder Jugendgruppen. Der frühe Kontakt zu diesen Gruppen ist uns sehr wichtig, um die App möglichst gut an die verschiedensten Bedürfnisse der zukünftigen NutzerInnen anzupassen und so die spätere Verbreitung der App zu unterstützen. Wenn auch Sie Interesse haben, uns dabei zu unterstützen, dann können Sie entweder an diesem Fragebogen teilnehmen oder wenden Sie sich direkt an Christina Ludwig. Aktuelle Informationen zu meinGrün finden Sie auch auf der meingrün Website. Die Aufgaben der Abteilung Geoinformatik der Universität Heidelberg betreffen die Weiterentwicklung der Ideen zu gesundem, angenehmen und grünem Routing auf Basis des Openrouteservice und der Analyse und Verbesserung der Qualität der OpenStreetMap Daten.

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

We cordially invite everybody interested to our next open GIScience colloquium talk

The speaker is Dr. Massimiliano Pittore
GFZ German Research Centre for Geosciences - Helmholz Centre Potsdam

When: Monday 20.05.2019, 2:15 pm

Where: INF 348, room 015 (Institute of Geography, Heidelberg University)

GIScience for GEOscience: challenges and opportunities in a risky world

The assessment of risk arising from the increasingly complex and vulnerable urban areas being exposed to natural hazards is a matter of cross-disciplinarity, patience and creativity, and is more and more a matter of geo-information. In order to match the requirements of practitioners and end-users, risk estimates have to precisely catch the relevant features of the built environment and its complex and vulnerable infrastructure. This requires the harmonisation of heterogeneous data sources, from remote sensing to authoritative and volunteered geoinformation, and calls upon new methodologies to collect, process and aggregate each “atomic” information into dynamic, multi-scale and uncertainty-aware models. The increasing availability of large scale loosely structured geoinformation paves the way to the application of (geo)statistical learning (artificial intelligence) techniques, but also advocates for a stronger cooperation among professionals with very different background. In this talk I will review some of our recent research activities where seismology, engineering and different flavours of GIScience are playing together to address trending issues in exposure and vulnerability modelling and in post-earthquake damage mapping.

Introduction
Exploring how OpenStreetMap data developed over time across different administrative untis might reveal interesting insights into the self organizing approach of the OSM communities and can potentially be used to derive intrinsic data quality indicators. It might even be possible to estimate the completeness of OSM for a specific key-value combination as done by Barrington-Leigh & Millard-Ball (2017) for the road network.

Here we want to investigate the development of health related amenities across countries. The focus of the post will be on exploration of the data to highlight a few interesting patterns. A scientific rigorous analysis is not the aim of the post but will follow in a dedicated scientific journal.

Data and methods
We queried the OSM history by using the OpenStreetMap History Data Analytics Platform - if you are unfamiliar with the ohsome platform (OSHDB and ohsome API) we encourage you to explore the related blog posts:

We queried ways and nodes but not relations for the key-value combinations provided later on, used national boundaries to group by and monthly time steps.

Results were stored in the postgresql database of the OSM history explorer (see the related blog post) and further analyzed by R & Rstudio using the packages from the tidyverse, ggplot2, forcats, stringr, RPostgreSQL and DBI.

Data exploration
Saturation type time series
If we look at France and Hungary it looks as if the number of hospitals has reached a peak which we might take as an indicator that the number of hospitals in both countries have been completely mapped (give or take a few). There have been some interesting ups and downs in Hungary.

Fitting a standard logistic saturation curve leads to reliable results. For France the estimated number of hospitals equals 2800 with a standard error of 11.3. For Hungary the estimated number of hospitals equals 333 with a standard error of 1.8.


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The Heidelberg LiDAR Operations Simulator (HELIOS) is an open source laser scanning simulation framework for interactive simulation and visualization of terrestrial, mobile and airborne laser scanning surveys. It can be flexibly used for teaching and training of laser scanning, development of new scanner hardware and scanning methods, or generation of artificial scan data sets to support the development of point cloud processing and analysis algorithms.

Due to its high flexibility it has already been used by the 3DGeo Research Group and other research groups for different studies and applications:

Validation of leaf angle distribution quantification

Evaluation of point cloud quality requirements for Scan-vs-BIM based automated construction progress monitoring

Understory forest structure modelling

Simulation of full-waveform laser scanning of outcrops for the development of point cloud analysis algorithms and survey planning

If you are interested in using HELIOS for your application and research, you can download a pre-compiled latest version of HELIOS here. The corresponding wiki contains detailed information on how to set up and handle HELIOS.

Missing features in HELIOS? Why not contribute to the project on GitHub - join as developer or submit “issues” you encounter!

Follow us on ResearchGate to keep updated on the HELIOS research project!

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