AI for assisting in natural disaster relief efforts: the xView2 competition

Satellite imagery combined with AI is a game-changing tool not only in many business scenarios but also in humanitarian efforts. The xView2 competition makes this a reality. It challenges participants to develop a computer vision model for supporting Humanitarian Assistance and Disaster Recovery organizations. In this post, I would like to give you an overview of the competition, what makes it special, and share why we decided to take part in it.
Overview of the problems and rationale for the competition
Climate change is exacerbating the risk of natural disasters with frequent, unpredictable changes in weather patterns. Logistics, resource planning, and damage estimation are difficult after the disaster hits and can be very expensive. We need good data to maximize the effectiveness of such efforts, especially in resource-constrained developing countries. Currently, the process is labor-intensive with specialists analyzing aerial and satellite data from state, federal, and commercial sources to assess the damage. Moreover, the data itself is often scattered, missing, or incomplete. The xView2 competition addresses both of these issues. On the one hand, it builds on a novel satellite imagery dataset xBD. xBD provides data on 8 different disaster types spanning 15 countries and covers an area of 45,000 square kilometers. Moreover, it introduces the Joint Damage Scale for labeling building damage, which provides guidance and an assessment scale to label building damage in a satellite image.

- It is frequent and timely (new data is available every day)
- Image quality is high (30x30cm of surface per pixel)
- Images go beyond visible light into other parts of the electromagnetic spectrum, which opens new opportunities for analysis unavailable to the human eye
- Historical data is easily available for comparisons across time

Overcoming challenges
Whilst the xView2 competition is likely to close the two major gaps in disaster relief planning and response – lack of suitable data for training machine learning models as well as labor intensiveness of the assessment process – we discovered a number of key challenges when working on the dataset:- Buildings pre and post-damage are photographed at different angles, which makes the task more difficult.
- Some of the buildings are located in densely inhabited areas, whilst others are free-standing houses. They also differ in size ranging from simple huts to large shopping centers.
- The data spans different types of disasters. This is an advantage for disaster relief purposes and the ultimate goal of the competition but constitutes an additional challenge for participants.
- The data is unbalanced – the dataset mostly contains undamaged buildings.
- The resolution of the images had been artificially reduced by the competition organizers, even though higher-quality data is readily available for some of the covered regions.
- The dataset does not contain non-visible light channels that can be helpful in achieving better accuracy (e.g. by helping detect water using NDVI value).
- We need to balance the depth and complexity of the network with training efficiency and cost. It is not easy to train a neural network on a vast amount of data in a cost-effective manner.