Abstract

Damaged Building footprint detection in satellite and aerial imagery is crucial in city management. Building detection is a fundamental but a challenging problem mainly because it requires correct recovery of building footprints from high-resolution images. Buildings are one of the key pieces of cadastral information related to population and cities, and are fundamental to urban planning & policymaking. Critical infrastructures, such as public transport, electricity, water distribution networks, or postal and delivery services, rely heavily on accurate population and building maps. On top of that, it is essential to get real-life, up-to-date information about buildings each time there is a need for disaster risk management, risk assessment, or emergency relief. Accurate and fine-grained information about the extent of damage to buildings is essential for directing Humanitarian Aid and Disaster Response operations in the immediate aftermath of any natural calamity. Satellite and UAV (drone) imagery has been used for this purpose in recent years, sometimes aided by computer vision algorithms. Existing Computer Vision approaches for building damage assessment typically rely on a two stage approach, consisting of building detection using an object detection model, followed by damage assessment through classification of the detected building tiles. These multi-stage methods are not end-to-end trainable, and as well as suffer from poor overall results. We proposed the UNet segmentation model, a model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end. We trained the model using X View 2 challenge dataset.

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