# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
## 2 Related Work  
In the twenty-first century, our social interactaction habits mainly revolve around smartphones and IoT-devices. The Internet in general and social media represent a new way for us to learn and communicate. In a personal Facebook or Twitter profile, it is easy to find personal information about daily activities, but also news about real-time events. During natural disasters, social media represents a huge source of information from which, if properly processed, it is possible to extract valuable data for emergency management organizations. Indeed, the research literature presents several studies to detect, collect and process valuable information [10; 11; 12]. Concerning flood events, general approaches aim to detect flood events [13; 14; 15], to segment water regions [16], or to estimate water level [17]. Other approaches aim to examine details, such as the presence of people [18; 19], the identification of the most affected areas [11], or the identification of flooded roads and their viability. This last topic is addressed in our work, and it is thought to be an extension of the approaches presented in the MediaEval 2018 conference. Therefore, this section continues introducing the methods submitted to the MediaEval challenge. The techniques are developed to deal with the two main kinds of data available from social media--metadata and images. As extensively described in Section 3, metadata are composed by textual information (e.g., text of the post, title) and punctual information (e.g., coordinates, post creation date, post author reference), while the images are PNG or JPG pictures. The metadata information was approached in many ways. A simple approach was proposed by [PERSON] et al. [20]. They manually created a set of rules which, leveraging on the textual part of the tweets, look for n-grams (subset of n contiguous words in the same sentence) representing strings of lexical items they would expect to occur in tweets related to road passability. Other works, such as the ones proposed by [PERSON] et al. [21] and [PERSON] et al. [22] started with a pre-processing of the tweet texts--first removing hyperlinks, punctuations and symbols and performing the word tokenization, then removing the stop-words and performing word stemming. The processed information was enriched by adding other metadata features, such as user tags. Another work by [PERSON] et al. [23] proposed to check the metadatalanguage feature and it increased the number of English tweets by translating the ones written in other languages. This simple step avoids the need to handle multiple languages simultaneously, which is still an open problem in the field of Natural Language Processing. To be properly processed by classifiers, words in tweets are then translated into numerical features. This step was made through the use of (i) pre-trained word embeddings, which convert words into numerical vectors, such as fasttext [20; 24], Word2 Vec [25] or GloVe [26], and/or (ii) statistical features, like Term Frequency--Inverse Document Frequency (TF-IDF) [27]. Numerical features are then used to train models such as Support Vector Machines (SVM) or Convolutional Neural Networks (CNNs) [28] for the final classification. Regarding the visual information, two approaches were mainly adopted on pictures: (i) using visual descriptors and (ii) extracting features from pre-trained CNNs. In the first case, the aim was to describe the images through a set of discrete information that could lead the classifier to improve the performances on the tasks. Several descriptors were already available from the dataset: Color and Edge Descriptor (CEDD) [29], Color Layout (CL) [30], Fuzzy Color and Texture Histrogram (FCTH) [31], Edge Histogram (EH) [32], Joint Composite Descriptor (JCD) [33] and Scalable Color Descriptor (SCD) [34]. In the latter case, hidden layers of CNNs are used as feature descriptors.