Deep Learning Models for Passability Detection of Flooded Roads

In this paper we study and compare several approaches to detect floods and evidence for passability of roads by conventional means in Twitter. We focus on tweets containing both visual information (a picture shared by the user) and metadata, a combination of text and related extra information intrinsic to the Twitter API. This work has been done in the context of the MediaEval 2018 Multimedia Satellite Task

AI-Based Flood Event Understanding and Quantifying Using Online Media and Satellite Data

In this paper we study the problem of flood detection and quantification using online media and satellite data. We present a three approaches, two of them based on neural networks and a third one based on the combination of different bands of satellite images. This work aims to detect floods and also give relevant information about the flood situation such as the water level and the extension of the flooded regions, as specified in the three subtasks, for which of them we propose a specific solution

Deep learning models for road passability detection during flood events using social media data

During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked / open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks at the same time. Both problems are treated as a single class classification problem by the usage of a compactness loss. The study is performed on a set of tweets, posted during flooding events, that contain (i)~metadata and (ii)~visual information. We study the usefulness of each source of data and the combination of both. Finally, we do a study of the performance gain from ensembling different networks. Through the experimental results we prove that the proposed double-ended NN makes the model almost two times faster and memory lighter while improving the results with respect to training two separate networks to solve each problem independently

Multi-modal Deep Learning Approach for Flood Detection

In this paper we propose a multi-modal deep learning approach to detect floods in social media posts. Social media posts normally contain some metadata and/or visual information, therefore in order to detect the floods we use this information. The model is based on a Convolutional Neural Network which extracts the visual features and a bidirectional Long Short-Term Memory network to extract the semantic features from the textual metadata. We validate the method on images extracted from Flickr which contain both visual information and metadata and compare the results when using both, visual information only or metadata only. This work has been done in the context of the MediaEval Multimedia Satellite Task

Resilient Hybrid SatCom and Terrestrial Networking for Unmanned Aerial Vehicles

Today, Unmanned Aerial Vehicles (UAVs) are widely used in many different scenarios including search, monitoring, inspection, and surveillance. To be able to transmit the sensor data from the UAVs to the destination reliably within tangible response times to the relevant content is crucial, especially for tactical use cases. In this paper, we propose network coded torrents (NECTOR) to leverage multiple network interfaces for resilient hybrid satellite communications (SatCom) and terrestrial networking for UAVs. NECTOR is significantly different from the state-of-the-art multipath protocols such as multipath TCP (MPTCP) as it does not require any additional packet scheduler, rate-adaptation or forward error correction. We present the design and implementation of NECTOR, and evaluate its performance compared to MPTCP. Our experimental results show that NECTOR provides goodput (up to 70%) higher than MPTCP with 5.49 times less signaling overhead