# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
## 6 Conclusions  
In this paper, we have extended the work conducted for the _Flood classification challenge_ in MediaEval 2018 in which we had presented a winning architecture based on a fine-tuning and ensembling of 45 models for each task. In this paper, we evaluate the evolution of the performance of the algorithm as we ensemble more models. It is determined that by ensembling models in order of average performance, we can obtain some improvements in terms of F1-Score for both tasks. However, as we ensemble networks, at some point, adding more networks to the ensemble starts worsening the results. Thus, by reducing the number of ensembled models to 30, we can reduce the number of ensembled networks, making the solution faster and lighter while improving the results. Then, we proposed a double-ended architecture that trains both tasks simultaneously to further reduce the number of parameters of the solution and to let the network share knowledge between tasks. We also propose the usage of a compactness loss, a loss proposed to convert a binary classifier network into a one-class classification network. In the original paper, the derivation of this loss contained a mistake which is corrected in this paper. Through experiments, we conclude that, by combining the double-ended architecture and the compactness loss, we are able to obtain a single network that solves both problems, achieving comparable results for the evidence of road task and better results  
Figure 12: Examples of tweets contained within the dataset that allowed to disambiguate the visual content for a correct ERP prediction. In (**a**–**e**) the text of the tweets allowed to resolve ambiguous ERP images thanks to keywords such as “flooded” in relation to roads and “drive through” in reference to vehicles.  
for the road passability estimation compared to the ensemble models. Since this solution does not rely on ensemble networks, it is almost 90 times faster and also lighter than the originally proposed architecture, making it a viable solution for a real-life application.  
Conceptualization, L.L.-F., A.F. and M.Z.; methodology, L.L.-F., A.F. and M.Z.; software, L.L.-F., A.F. and M.Z.; validation, L.L.-F., A.F., M.Z., H.S. and P.G.; formal analysis, L.L.-F., A.F., M.Z., H.S. and P.G.; investigation, L.L.-F., A.F. and M.Z.; resources, L.L.-F., A.F. and M.Z.; data curation, L.L.-F. and M.Z.; writing--original draft preparation, L.L.-F., A.F., M.Z., H.S. and P.G.; writing--review and editing, H.S. and P.G.; visualization, L.L.-F., A.F. and M.Z.; supervision, H.S. and P.G.; funding acquisition, L.L.-F., A.F. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the Spanish Grant FEDER/Ministerio de Economia, Industria y Competitividad - AEI/TIN2016-75404-P and the European Commission H2020 FASTER project no. 833507. The APC was funded by University of the Balearic Islands.  
[PERSON] benefits from the NAERINGSPHD fellowship of the Norwegian Research Council under the collaboration agreement Ref.3114 with the UIB.  
The authors declare no conflict of interest.