Conference paper Open Access

Road passability estimation using deep neural networks and satellite image patches

Moumtzidou Anastasia; Bakratsas Marios; Andreadis Stelios; Gialampoukidis Ilias; Vrochidis Stefanos; Kompatsiaris Ioannis

Artificial Intelligence (AI) technologies are getting deeper and deeper into remote sensing and satellite image processing offering value-added products and services in a real-time manner. Deep learning techniques applied on visual content are able to infer accurate decisions about concepts and events in an automatic way, based on Deep Convolutional Neural Networks which are trained on very large external image collections in order to transfer knowledge from them to the considered task. Existing emergency management services focus on the detection of flooded areas, without the possibility to infer if a road from point A to a point B is passable or not. To that end, we propose an automatic road passability service that is able to deliver the parts of the road network which are not passable, using satellite image patches. Experiments and fine-tuning on an annotated benchmark collection indicates the most suitable model among several Deep Convolutional Neural Networks.

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