Satellite Damage Assessment using Machine Learning

Satellite Damage Assessment using Machine Learning

Improving Response Times Saves Lives

If you’ve ever crashed a car before then you know how it awkward and frustrating it can be. If you’re lucky and everything is straight forward, then it’s alright but if not, you can be in for a world of pain. If you’ve ever been through a natural disaster though, it’s just a pain from start to end.

First comes the blame game, then comes the proof

From the perspective of the Government and the Insurance industry, the use case is quite obvious as the occurrence of disasters such as hurricanes, earthquakes, floods need to be identified quickly. These kind of events don’t just decimate the buildings when they occur, but they decimate the entire environment around it too.

Obtaining accurate data to help plan for an effective response has been a challenge because collecting and extracting data has been a slow and labour intensive operation. Given that, any response is currently quite slow. Drones and Satellite Imagery have improved the problem somewhat, but a lot of data still has to be manually collated.

As a result of this problem, machine learning fits naturally here to automate the role of detecting damages caused by disasters. Machine Learning can also offer a solution for future disasters because a well built model can generalise, but also improve it’s recognition capability as the amount of data increases.

Existing work in the field focuses largely on single-event disasters but recently, Google produced a custom dataset spanning 3 disasters (Haiti, Mexico and Indonesian earthquakes), from which they applied a CNN and measured how well it could generalise.

The paper, Building Damage Detection in Satellite Imagery Using Convolutional Neural Networks takes this challenge to create a system that can (a) recognise damaged buildings but more importantly (b) generalise across different disasters.

As a first step, data needed to be generated to train the model, which is split using the following process:

The data is first generated is split into two steps: building detection and damage classification.

From here, different forms of neural network architectures are considered, where each architecture is a variant of an AlexNet.

AlexNet

An AlexNet uses a sequence of convolutional layers followed by a sequence of fully connected layers and finally a sigmoid layer as output.

Note: The AlexNet here can be reproduced using the following github repository [ source ]

AlexNet is famous from the 2012 ImageNet competition, where Hinton et al demonstrated that the performance of a CNN is l argely related to the depth of the network .

Aside from this, the problem is a bit more difficult as images are distorted by blurriness, obfuscation, colour differences, and other features. These issues can make it more difficult for any image recognition system to recognise broken buildings, to a histogram equalisation technique can be used to normalise between images before testing.

On a single event setting, the accuracy of the model is quite high but the more important result is that results from one disaster demonstrate predictability natural disasters.

Once a neural network is trained, it’s relatively efficient to pick up and apply to a new data set which when applied to natural disasters, reduces time, energy and the effort required of crisis workers to generate disaster reports. Meanwhile, a timely decision on aid delivery can be implemented without any delay.

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