Humanitarian AI applications for satellite imagery

 

Project goal

This project is making use of expertise in artificial intelligence (AI) technologies at CERN to support a UN agency. Specifically, we are working on AI approaches to help improve object recognition in the satellite imagery created to support humanitarian interventions. Such satellite imagery plays a vital role in helping humanitarian organisations plan and coordinate responses to natural disasters, population migrations, and conflicts.

R&D topic
Applications in other disciplines
Project coordinator(s)
Sofia Vallecorsa, Federico Carminati
Team members
Taghi Aliyev, Yoann Boget
Collaborator liaison(s)
Lars Bromley

Collaborators

Project background

Since 2002, CERN has hosted UNOSAT, the Operational Satellite Applications Programme of UNITAR (The United Nations institute for Training and Research) on the laboratory’s premises. UNOSAT acquires and processes satellite data to produce and deliver information, analysis, and observations to be used by the UN or national entities for emergency response, to assess the impact of a disaster or a conflict, or to plan sustainable development in the face of climate change.

At the heart of this project lies the idea of developing machine-learning techniques that can help speed up analysis of satellite imagery. For example, predicting and understanding the movement of displaced persons by identifying refugee shelters can be a long, labour-intensive task. This project is working to develop machine-learning techniques that could greatly reduce the amount of time needed to complete such tasks.

Recent progress

Refugee camps often consist of more than 10,000 shelters and may need to be re-analysed several times in order to understand their evolution. Manual analysis typically leads to very high-quality output, but is very time-consuming. We have therefore worked with region-based convolutional neural networks to improve detection of new shelters in refugee camps, taking into account prior knowledge regarding the positions of existing shelters. The results were promising and the data pipeline created by our summer students has now been adapted and put to use by the UNOSAT experts. The retrained model yielded an average precision/recall score of roughly 80% and reduced the time needed for the task by a factor of 200 in some areas.

More recently, we also addressed the challenge of simulating synthetic high-resolution satellite images. High-resolution satellite imagery is often licensed in such a way that makes it difficult to share it across UN partners and academic organizations. This reduces the amount of image data available to train deep-learning models, thus hampering research in this area. We have developed a generative adversarial network (GAN) that is capable of generating realistic satellite images of refugee camps images. Our tool was initially based on a progressive GAN approach developed by NVIDIA. We have now developed this further, such that it can combine multiple simulated images into a cohesive larger image of roughly 5 million pixels.

Several other lines of investigation — all related to AI technologies — are also being pursued within the scope of this project.

Next steps

Next year, we will pursue the initial work carried out on the GAN model in 2019 in a number of different directions. We will carry out a detailed performance study and will implement a distributed approach for parallel network training, as well optimising the use of computing resources. This should help us to reduce training time for the model and increase the maximum image size.

Publications

    N. Lacroix, T. Aliyev, L. Bromley: Automated Shelter Recognition in Refugee Camps. CERN openlab Summer Student Report. Published on ZENODO, 2019. http://cern.ch/go/v6rn

Presentations

    Y. Boget, ProGAN on Satellite images (15 August). Presented at CERN openlab summer student lightning talk session, Geneva, 2019. cern.ch/go/P6NV
    Y. Boget, S. Vallecorsa, Deep Learning for Satellite Imagery (24 September). Presented at IXPUG Annual Conference, Geneva, 2019. cern.ch/go/m9n6