# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
## 1 Introduction  
In the last decades, the frequency and intensity of natural disasters has risen significantly. According to worldwide data from the Centre for Research on the Epidemiology of Disasters [1], about 12 times more natural disasters events were registered in 2017 compared to in 1950. In Figure 1, the dramatic increase in these events is shown, which comprise mass movements, volcanic activities, wild-fires, landslides, earthquakes, extreme temperatures, droughts, extreme weather, floods and epidemics. It can be noted that flood events were particularly frequent; in 2017 alone, floods represented approximately 39% of global natural disasters. On top of the tragic loss of human lives and infrastructures, natural disasters come at a large cost to governments. The European Commission (EC) estimated that, since 2005, natural disasters have cost the European Union (EU) close to 100 billion euros. However, this cost can be significantly reduced by investing in risk prevention: the EC stated that for every 1 euro spent on prevention, 4 euros or more could be saved in response. In this respect, with the \"cohesion policy\", the EU allocated 8 billion euros for climate change adaptation, risk prevention and management over the 2014-2020 period [3]. Those investments generated several projects and research opportunities, from which this work is taken. The ability to detect the insurance of such problems in a timely manner is a powerful tool for the bodies in charge of protecting and guaranteeing citizens' safety. In this work, we are going to focus on flood events and specifically, on assessing the status of roads after floods, since knowing the best route to access the affected areas is crucial to transport emergency support to victims. This can be done via analysis of photos from affected areas with known location. Such photos can be: (i) solicited via dedicated apps, such as UN-ASIGN [4, 5] and I-REACT [6, 7], or (ii) harvested from unsolicited sources such as social media, as people frequently share pictures during emergencies. The use of apps and social media to engage the civil population is of increasing interest and can be useful for first responders. Certainly, social media is not largely known and adopted as an emergency reporting tool, but there is evidence [8] of a large number of posts that provide direct proof of occurring natural disasters, which, if properly processed, could help in the handling of the emergencies. The need for sensibility regarding natural disasters and the variety of data to deal with make the research community itself an active player in those topics and it generates numerous important and effective conferences. The objective is, given a collection of posts (including images) related to floods, determining whether: (i) there is _Evidence of Roads_ and, in a positive case, (ii) there is _Evidence of Road Passability_. In the first case, we are more interested in asserting the presence of a road in the picture: this means the road can be directly visible or there are enough elements justifying its existence, such as the presence  
Figure 1: The occurrence of natural disasters and flood events since 1950. The natural disasters set is characterized by the following phenomena: mass movements, volcanic activities, wildfires, landslides, earthquakes, extreme temperatures, droughs, extreme weather, floods and epidemics. The chart shows a sensible increase in this kind of events, which, by 2017 has increased by over a factor of 10 with respect to 1950. Another important aspect is the percentage of incidence of flood events, representing, on average, 30% of the overall natural disasters [2]. of traffic lights or vertical signs. On the other hand, the second goal aims to determine whether the identified road is in good condition to be transited. In the flood context, the evidence of road passability means that the road is completely clean or it can be partially or totally covered by water, but there must be evidence that vehicles or people can still pass through it.