Building Missing Maps with Machine Learning
We are in a period of increasing humanitarian crises, both in scale and number. Natural disasters continue to increase in frequency and impact, while long-term and reignited conflicts affect people in many parts of the world. Often, accurate maps either do not exist or are outdated by disaster or conflict.
Humanity & Inclusion is an aid organization working in some 60 countries, alongside people with disabilities and vulnerable populations. Our emergency sector responds quickly and effectively to natural and civil disasters.
Many parts of the world have not been mapped; especially the most marginalized parts, that is, those most vulnerable to natural hazards. Obtaining maps of these potential crisis areas greatly improves the response of emergency preparedness actors.
During a disaster it is extremely useful to be able to map the impassable sections of road for example, as well as the most damaged residential areas, the most vulnerable schools and public buildings, population movements, etc. The objective is to adapt as quickly as possible the intervention procedures to the evolution of the context generated by the crisis.
In the first days following the occurrence of a disaster, it is essential to have as fine a mapping as possible of communication networks, housing areas and infrastructures, areas dedicated to agriculture, etc.
Today, when new maps are needed they are drawn by hand, often by volunteers who participate in so called Mapathons. They draw roads and buildings on satellite images, and contribute to Open StreetMap.
Humanity & Inclusion is the world’s most comprehensive mine action charity. The heart of this action is victim assistance, risk education activities, stockpile management and, advocacy to ban landmines and cluster bombs. But teams also work with local authorities, to clear landmines and other explosive remnants of war.
Recently we organized a Mapathon to draw new maps for our clearance teams in Laos.
Satellite imagery is readily available to humanitarian organisations, but translating images into maps is an intensive effort. Today maps are produced by specialized organisations or in volunteer events such as mapathons, where imagery is annotated with roads, buildings, farms, rivers etc.
Images are increasingly available from a variety of sources, including nano-satellites, drones and conventional high altitude satellites. The data is available: the task is to produce intervention-specific maps with the relevant features, in a short timeframe and from disparate data sources.
Tech4Dev 2018 International Conference
The UNESCO Chair in Technologies for Development hosted by the Cooperation & Development Center (CODEV) at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland is pleased to announce the 5th International Conference on Technologies for Development (Tech4Dev 2018) taking place from 27-29 June 2018 at the SwissTech Convention Center in Lausanne, Switzerland.
The Tech4Dev Conference is the biennial flagship event of the UNESCO Chair in Technologies for Development hosted by CODEV at EPFL. The Conference focuses on the potential of technology solutions to advance inclusive social and economic development in the Global South.Tech4Dev 2018 puts the challenges and the potential of the Global South in the center of discussions reflected by its title Voices of the Global South. Indeed, even though the Sustainable Development Goals apply globally, the needs in terms of access to health and financial services, functioning education systems and sustainable urban and natural ecosystems are undeniably more significant in the Global South. In this perspective, it is crucial to listen to and support stakeholders from the regions who are facing these challenges.
Winners of the Mapping Challenge will be invited, with travel and accommodation paid, to demonstrate and present their solution for the challenge within the GHL exhibition space.
The first round of this challenge will be based on the publicly available SpaceNet database of labeled images, using the Khartoum dataset with a resolution of 30cm. Participants will be expected to identify buildings and roads, which will then be assessed against a subset of the images.
A Docker container and predictions file in geojson format will be submitted for evaluation by crowdAI.
Round 1 will complete on March 29th, at 09:00 GMT+1.
Datasets, sample code and submission guidelines will be released soon.
The top 20 winners of Round 1 will be invited to complete in Round 2, and the Docker containers for this group will be executed and compared against the submitted predictions file.
The top scoring 20 participants in Round 1 will be invited to participate in round 2. For this round a new dataset will be used to evaluate the submissions. A dataset more closely meeting the end requirements of the NGO activities will be evaluated against.
In this Round participants will submit Docker containers of their work, based on a provided template. These containers will be executed by crowdAI and the resulting scores presented on the Leaderboard.
Round 2 will complete on May 29th, at 09:00 GMT+1.
The 2018 International Tech4Dev Conference is from 27-29th June, 2018.
- Invitation to present the winning solution at the Global Humanitatian Lab’s exhibit space at the 2018 Tech4Dev Event, with travel and accommodation covered.
- Invitation to the 3nd Applied Machine Learning Days at EPFL in Switzerland in January 2019, with travel and accommodation covered.
- A printed award of appreciation from the GHL, Handicap International and CarteONG.