@article {28311,
	title = {Visual Sentiment Analysis from Disaster Images in Social Media},
	journal = {Sensors},
	volume = {22},
	number = {10},
	year = {2022},
	month = {05/2022},
	pages = {3628},
	publisher = {MDPI},
	abstract = {The increasing popularity of social networks and users{\textquoteright} tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people{\textquoteright}s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.},
	doi = {10.3390/s22103628},
	url = {https://doi.org/10.3390/s22103628},
	author = {Syed Zohaib {Hassan} and Ahmad, Kashif and Hicks, Steven and Halvorsen, P{\r a}l and Al-Fuqaha, Ala and Conci, Nicola and Riegler, Michael}
}
@inproceedings {42767,
	title = {Visual Sentiment Analysis: A Natural Disaster Use-case Task at MediaEval 2021},
	journal = {MediaEval 2021},
	year = {2021},
	publisher = {Ceurws},
	author = {Syed Zohaib {Hassan} and Ahmad, Kashif and Riegler, Michael and Hicks, Steven and Conci, Nicola and Halvorsen, P{\r a}l and Al-Fuqaha, Ala}
}
@article {26455,
	title = {Automatic detection of passable roads after floods in remote sensed and social media data},
	journal = {Signal Processing: Image Communication},
	volume = {74},
	year = {2019},
	pages = {110-118},
	publisher = {Elsevier},
	abstract = {This paper addresses the problem of floods classification and floods aftermath detection based onboth social media and satellite imagery. Automatic detection of disasters such as floods is still a very challenging task. The focus lies on identifying passable routes or roads during floods. Two novel solutions are presented, which were developed for two corresponding tasks at the MediaEval 2018 benchmarking challenge. The tasks are (i) identification of images providing evidence for road passability and (ii) differentiation and detection of passable and non-passable roads in images from two complementary sources of information. For the first challenge, we mainly rely on object and scene-level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are then combined using early, late and double fusion techniques. To identify whether or not it is possible for a vehicle to pass a road in satellite images, we rely on Convolutional Neural Networks and a transfer learning-based classification approach. The evaluation of the proposed methods is carried out on the large-scale datasets provided for the benchmark competition. The results demonstrate significant improvement in the performance over the recent state-of-art approaches.},
	keywords = {convolutional neural networks, Flood detection, Multimedia Indexing and Retrieval, Natural Disasters, Satellite Imagery, Social Media},
	doi = {https://doi.org/10.1016/j.image.2019.02.002},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola and Dahyot, Rozenn}
}
@inproceedings {26259,
	title = {Deep learning approaches for flood classification and flood aftermath detection},
	journal = {Working Notes Proceedings of the MediaEval 2018 Workshop},
	volume = {2283},
	year = {2018},
	publisher = {CEUR-WS.org},
	address = {Sophia Antipolis, France},
	abstract = {This paper presents the method proposed by team UTAOS for MediaEval 2018 Multimedia Satellite Task: Emergency Response for Flooding Events. In the first challenge, we mainly rely on object and scene level features extracted through multiple deep models pre-trained on the ImageNet and Places datasets. The object and scene-level features are combined using early, late and double fusion techniques achieving an average F1-score of 65.03\%, 60.59\% and 63.58\%, respectively. For the second challenge, we rely on a convolutional neural networks (CNNs) and a transfer learning-based classification approach achieving an average F1-score of 62.30\% and 61.02\% for run 1 and run 2, respectively.},
	author = {Said, Naina and Pogorelov, Konstantin and Ahmad, Kashif and Riegler, Michael and Ahmad, Nasir and Ostroukhova, Olga and Halvorsen, P{\r a}l and Conci, Nicola}
}
@article {26257,
	title = {Social Media and Satellites. Disaster event detection, linking and summarization},
	journal = {Multimedia Tools and Applications},
	volume = {78},
	year = {2018},
	pages = {2837{\textendash}2875},
	publisher = {Springer},
	address = {US},
	abstract = {Being able to automatically link social media and satellite imagery holds large opportunities for research, with a potentially considerable impact on society. The possibility of integrating different information sources opens in fact to new scenarios where the wide coverage of satellite imaging can be used as a collector of the fine-grained details provided by the social media. Remote-sensed data and social media data can well complement each other, integrating the wide perspective provided by the satellite view with the information collected locally, being it textual, audio, or visual. Among the possible applications, natural disasters are certainly one of the most interesting scenarios, where global and local perspectives are needed at the same time.In this paper, we present a system called JORD that is able to autonomously collect social media data (including the text analysis in local languages) about technological and environmental disasters, and link it automatically to remote-sensed data. Moreover, in order to ensure the quality of retrieved information, JORD is equipped with a hierarchical filtering mechanism relying on the temporal information and the content analysis of retrieved multimedia data.To show the capabilities of the system, we present a large number of disaster events detected by the system, and we evaluate both the quality of the provided information about the events and the usefulness of JORD from potential users viewpoint, using crowdsourcing.},
	keywords = {Event Detection, Information retrieval, Natural Disaster, Social Media},
	doi = {10.1007/s11042-018-5982-9},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l}
}
@inproceedings {25629,
	title = {CNN and GAN Based Satellite and Social Media Data Fusion for Disaster Detection},
	journal = {MediaEval Benchmark 2017},
	year = {2017},
	month = {09/2017},
	publisher = {CEUR-WS.org},
	address = {Dublin, Ireland},
	author = {Ahmad, Kashif and Pogorelov, Konstantin and Riegler, Michael and Conci, Nicola and Halvorsen, P{\r a}l},
	editor = {Ahmad, Kashif}
}
@inproceedings {25812,
	title = {JORD: A System for Collecting Information and Monitoring Natural Disasters by Linking Social Media with Satellite Imagery},
	journal = {Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing  - CBMI {\textquoteright}17},
	year = {2017},
	pages = {12:1--12:6},
	publisher = {ACM Press},
	address = {New York, USA},
	abstract = {Gathering information, and continuously monitoring the affected areas after a natural disaster can be crucial to assess the damage, and speed up the recovery process. Satellite imagery is being considered as one of the most productive sources to monitor the after effects of a natural disaster; however, it also comes with a lot of challenges and limitations, due to slow update. It would be beneficiary to link remote sensed data with social media for the damage assessment, and obtaining detailed information about a disaster. The additional information, which are obtainable by social media, can enrich remote-sensed data, and overcome its limitations. To tackle this, we present a system called JORD that is able to autonomously collect social media data about natural disasters, and link it automatically to remote-sensed data. In addition, we demonstrate that queries in local languages that are relevant to the exact position of natural disasters retrieve more accurate information about a disaster event. We also provide content based analysis along with temporal and geo-location based filtering to provide more accurate information to the users. To show the capabilities of the system, we demonstrate that a large number of disaster events can be detected by the system. In addition, we use crowdsourcing to demonstrate the quality of the provided information about the disasters, and usefulness of JORD from potential users point of view.},
	isbn = {9781450353335},
	doi = {10.1145/3095713.3095726},
	url = {http://dl.acm.org/citation.cfm?doid=3095713},
	author = {Ahmad, Kashif and Riegler, Michael and Pogorelov, Konstantin and Halvorsen, P{\r a}l and De Natale, Francesco}
}
@inproceedings {25269,
	title = {The JORD System - Linking sky and social multimedia data to Natural and Technological Disasters},
	journal = {ACM International Conference on Multimedia Retrieval},
	year = {2017},
	month = {06/2017},
	publisher = {ACM},
	address = {Bucharest},
	isbn = {978-1-4503-4701-3},
	doi = {10.1145/3078971.3079013},
	author = {Ahmad, Kashif and Riegler, Michael and Riaz, Ans and Conci, Nicola and Dang-Nguyen, Duc-Tien and Halvorsen, P{\r a}l},
	editor = {Ionescu, Bogdan and Sebe, Nicu}
}
