Each significant seismic event is an opportunity to assess the performance of our built environment. After such events, a large quantity of varied information is often collected within a short time period. Therefore, a quick and reliable method for detecting damage to buildings is required for disaster response with the intent of reducing both human and economic losses. In the last few years, the increasing advancement of technologies and Computer Science has permitted the adoption of Machine Learning techniques in various fields, included Civil Engineering and Urban Resilience. One of the most influence parameters in the evaluation of seismic loss is the extension of the debris and their associated effects on critical infrastructure.