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.
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.
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.
Deeply 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.
A service providing real-time OSM data.