T-REX On The Move

to be met: daylight, cloudless weather, appropriate position of the satellite and free time in the satellite imaging schedule.

“The technology used to detect damaged buildings in areas not accessible by drones or planes is our standard product modified and optimized for the detection of damage caused by shelling”, said Władysław Surała, Head of Partnerships, Tensorflight.

“We rely on high resolution satellite imagery analyzed by neural networks trained specifically to detect building damage. The images are taken from the top (~0 degrees from the axis) and oblique (30-50 degrees from the axis) so as to not omit damaged buildings with roofs still intact. As we collect more imagery of urban war zones and have more examples to re-train the model, our models will incrementally improve,” he added.

Surala explains that after the imagery is delivered to their servers, they pre-process to sharpen it. If there is auxiliary data available, such as footprints of the buildings, this is the step when they prepare it - they load the coordinates of the footprints to help detect the buildings. Next, the image is loaded into the neural networks, designed and trained to detect the buildings, and then they are classified as damaged or intact.

Tensorflight’s Deep Learning models then estimate the level of the damage to the buildings. This is further categorized into classes indicating the degree of damage, e.g. from completely demolished, to suitable for repair. On top of that, the engine can load historical data about the damaged building and estimate the cost of reconstruction - this is also part of their standard product.

“Another area of our project’s expansion is accounting for more complex types of damage and losses - ecological, de-mining, lost harvest, movable property, and many more.

A very sad example is damage to the properties of deceased owners. The next stages of our work will gradually cover these areas,” Nefyodov added.

Nibedita Mohanta

Chief Sub Editor Geospatial World

Nibedita@geospatialworld.net

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