Geoinformation for Humanitarian Aid
What we do
A pivotal supplementation for official geodata and remote sensing data, user-generated spatial data such as those provided by the OpenStreetMap project or the Social Web are becoming ever more important for efficient crisis management. By mashing different datasets, the resulting situation intelligence enables humanitarian relief organizations and first responders to quickly gain awareness of the situation in the aftermath of a disaster event.
This information also plays a pivotal role in the efforts to mitigate risks, prepare for events, and limit their potential effects. In cooperation with users and relief workers, we are developing innovative processes and services for new analysis methods to better use this potential of different data sources. You can find our research papers in the publications list.
Humanitarian OSM Stats is a project which aims to present statistics and numbers about mapping in OpenStreetMap (OSM) for humanitarian purposes.
The Humanitarian OpenStreetMap Team (HOT) applies open mapping in OSM towards humanitarian action and community development. The open-source Tasking Manager hosted by HOT ist the major tool used to coordinate the efforts of thousands of remote mappers. The statistics rely on a dump of the Tasking Manager database, which is provided by HOT once a week.
On Humanitarian OSM Stats statistics of three main subject areas are presented:
This part shows statistics of activity based on sessions. You get the number of projects, contributors and countries involved. On a map the mapping activity for each project on a monthly basis is visualized. Further graphs give insight to the mapping activity and status of the projects.
In this part contributions to the OSM database analyzed with OSHDB are presented. You see the actual amount of buildings and highways added to OSM in general compared to those added via the Tasking Manager.
In this section Humanitarian OSM Stats shows the community behind the mapping and who is contributing how much. One graph shows how many percent of the userbase were active on at least x days. Another shows how many percent of users combined do how much of the total work. How experienced the users that mapped were is shown, too.
Collaboration HeiGIT - German Red Cross
The partnership between the German Red Cross (GRC) and HeiGIT was founded in 2017 with the aim to develop GIS solutions for the implementation of humanitarian activities of the Red Cross and Red Crescent Movement. At the same time, the knowledge gained from this partnership is incorporated into the work of HeiGIT. A central component of the cooperation is the regular exchange between the HeiGIT team and the GRC about the current operational requirements, problems and new findings in the field of humanitarian aid as well as about the support in disaster prevention and forecast-based financing. The cooperation is strengthened by the joint project “25 Mapathons” – financed by the Klaus-Tschira Foundation.
In the course of the cooperation between the GRC and HeiGIT, a specialist office for geoinformatics financed by the Klaus Tschira Foundation could be created within the team International Cooperation of the GRC.
Missing Maps is an initiative of various humanitarian organizations with the goal of mapping missing map information in areas threatened by (natural) disasters in OpenStreetMap (OSM) even before a disaster occurs. This geodata can then be used for prevention measures in the run-up to natural disasters or, in the event of a disaster, to support the work of local and international aid organizations. Both HeiGIT and the GRC are members of the Missing Maps Initiative.
The project “25 Mapathons”, funded by the Klaus Tschira Foundation, aims to raise internal awareness of the potential of geoinformatics within the GRC and to collect relevant geodata for GRC projects. For this purpose, HeiGIT and the GRC organize events for GRC divisions and the Youth Red Cross in order to give an insight into the international work of the GRC and to collect map data for operational areas. This takes place in the form of jointly organized mapathons, events in which helpers jointly map areas not previously recorded in OSM. One product of this project is the manual “How to organize a mapathon” with the aim to help GRC divisions to organize mapathons themselves.
Evaluation of the Mapathons
Mapathons are intended to generate high-quality data on the one hand, and to motivate volunteers to participate in OSM, especially in the field of humanitarian aid, on the other. Ideally, the Mapathon will arouse interest and lead to participants becoming permanently involved as contributors to OSM. Therefore, an evaluation of the Mapathons is planned to find out which type of Mapathon implementation can motivate participants for mapping for OSM in the long run. Since individual previous knowledge and interests – such as experience in geoinformatics or an affinity for technology – influence the results, the analysis will check for these factors.
Forecast-based financing offers a new approach to humanitarian aid. Based on weather forecasts and risk analyses, predefined measures are initiated as soon as a specific threshold is reached. The goal of these measures is to minimize the consequences of e.g. extreme weather events and to save human lives by taking precautionary measures instead of limiting oneself to the help after the occurrence of the disaster. In December 2020, the Anticipation Hub will be launched – a platform for the exchange of information between the forecast-based financing community and HeiGIT will join as a partner.
The routing service enables users to plan routes based on up-to-date OpenStreetMap data, all while taking into account disaster-related road conditions. This way, the on-site response teams always have access to continuously updated information about reachability and navigability of the surrounding roads.
Avoid Blocked Roads
After the 2015 earthquakes in Nepal, the global OpenStreetMap community mapped up to 800 road kilometers – every hour! This included information about blocked and impassable roads. We render these data usable for evacuation and response planning.
We currently provide hourly update intervals for geodata of all of Africa, South America, and South Asia. During larger disasters affecting regions not yet covered by the service, the addition of new areas can be requested.
Leverage Fleet Scheduling for Disaster Response
A complex example of routing optimization would be the distribution of goods by a fleet of multiple vehicles to dozens of locations. Visit our example of real-world scenario of distributing medical goods during disaster response.
Healthcare Access Analysis in Madagascar
The access to health facilities can be highly unequal within a country. Consequently, some areas and communities are more vulnerable to disasters effects than others. This notebook gives an overview on health sites distribution and the amount of population with access to those by foot and by car for Madagascar.
Road network critical assessment for disaster management
With the increasing accumulation of extreme weather events and environmental disasters, the need for quickly available information about the street infrastructure of cities and the accessibility of hospitals gains impotance. The road network can be analyzed with help of the isochronous functionality of the openroutservice and intrinsic data quality analyzes based on ohsome API of the HeiGIT. This enables efficient evaluations of the road network in disaster situations.
Nowadays, Machine Learning and Deep Learning approaches are steadily gaining popularity within the humanitarian (mapping) community. With our cooperating partner team at GIScience Research Group Heidelberg University we investigated the potential of Deep Learning in combination with MapSwipe’s crowdsourcing approach. To this end, we propose a novel workflow to combine deep learning (DeepVGI) and crowdsourcing (MapSwipe). Our strategy for allocating classification tasks to deep learning or crowdsourcing is based on confidence of the derived binary classification.
Deep learning from satellite imageries
Deep learning techniques, esp. Convolutional Neural Networks (CNNs), are now widely studied for predictive analytics with remote sensing images, which can be further applied in different domains for ground object detection, population mapping, etc. These methods usually train predicting models with the supervision of a large set of training examples.
Fill data gaps
The scarce availability of accurate and up-to-date human settlement data remains a major challenge, e.g., for humanitarian organizations. We investigate the complementary value of crowdsourcing and deep learning to fill the data gaps of existing earth observation-based (EO) products.
Interaction between human beings and machines
VGI data from OpenStreetMap and the mobile crowdsourcing application MapSwipe which allows volunteers to label images with buildings or roads for humanitarian aids are utilized. We develop an active learning framework with deep neural networks by incorporating both VGI data with more complete supervision knowledge.
|– improvement and extension of the Sketch Map Tool, for real use by the German Red Cross|
|– detection of thawing permafrost in the Arctic by citizen scientists (especially school students)|
|– global disaster risk reduction dataset|
|– comparing the accessibility of healthcare facilities around the globe|
|– support for Missing Maps in monitoring and visualizing its performance and impact using information from HOT Tasking Manage|
|– assessing the quality of health-related data in OpenStreetMap|
Cooperation Projects with HeiGIT Support
|– HeiGIT’s Map of Hope provides a geographic overview of planned, ongoing and completed clinical trials|
|– creating new remote employment opportunities for individuals affected by COVID-19 lock downs|
|– MapSwipe extension to monitor changes in satellite imagery|
Completed Projectse with HeiGIT Support
|– spatial location and retrieval of information on the topics of energy transition and sustainability|
A service providing real-time OSM data.