Damjan Hatic

"Machine learning methods for analysis of post catastrophic crisis areas"

Currently, with ever increasing occurrences of natural disasters, crisis response is a burning topic for the remote sensing community. Opportune reaction times and high-quality crisis coverage data of such events can make the difference between a well-orchestrated crisis response and a disaster. Even with the advancements of disaster prediction methods the responses are carried out according to crisis management plans and data is collected on the field. This is especially true for the parts of the world that have inconsistent satellite coverage and where crisis assessment is done by analysing UAV obtained images, since satellite imagery can be sparse. Current practices involve heuristic methods of image acquisition via UAVs and mostly heuristic image analysis. This insight is further used to fine tune the response to areas that need the most help. Using a combination of well-established GIS solutions, publicly available research satellite imagery and newer machine learning approaches for segmentation and object detection the quality and speed of acquiring crisis insight can be greatly improved.

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