Oil spill is considered one of the main threats to marine and coastal environments. Efficient monitoring and early identification of oil slicks are vital for the corresponding authorities to react expediently, confine the environmental pollution and avoid further damage. Synthetic aperture radar (SAR) sensors are commonly used for this objective due to their capability for operating efficiently regardless of the weather and illumination conditions. Black spots probably related to oil spills can be clearly captured by SAR sensors, yet their discrimination from look-alikes poses a challenging objective. A variety of different methods have been proposed to automatically detect and classify these dark spots. Most of them employ custom-made datasets posing results as non-comparable. Moreover, in most cases, a single label is assigned to the entire SAR image resulting in a difficulties when manipulating complex scenarios or extracting further information from the depicted content. To overcome these limitations, semantic segmentation with deep convolutional neural networks (DCNNs) is proposed as an efficient approach. Moreover, a publicly available SAR image dataset is introduced, aiming to consist a benchmark for future oil spill detection methods. The presented dataset is employed to review the performance of well-known DCNN segmentation models in the specific task. DeepLabv3+ presented the best performance, in terms of test set accuracy and related inference time. Furthermore, the complex nature of the specific problem, especially due to the challenging task of discriminating oil spills and look-alikes is discussed and illustrated, utilizing the introduced dataset. Results imply that DCNN segmentation models, trained and evaluated on the provided dataset, can be utilized to implement efficient oil spill detectors. Current work is expected to contribute significantly to the future research activity regarding oil spill identification and SAR image processing.

Although Augmented Reality (AR) technology has entered many market and knowledge domains such as games and leisure activities, it remains rather limited in digital heritage. After studying the potentiality of using modern AR elements in a museum context, this paper proposes the use of additional game and educational elements in the core AR application in order to enhance the overall on-the-spot museum visitor’s experience. An agile AR application design methodology was followed by taking into account the needs of small-to-medium sized real-world museums. Moreover, a heuristic evaluation protocol was applied by a group of experts in order to test the proof-of-concept AR application, in which some novel elements were proposed such as the AR quiz game. The main findings indicate that enhanced AR experiences in museum settings can make a nice fit with the user environment, physical and perceptual abilities, known metaphors, and user position and motion in 3D space. Moreover, AR services can be provided under a minimum distraction and physical effort. As a conclusion, AR technologies are mature enough to be standardized for museum usage, while the audience seems to be ready to take advantage of the related enhanced museum experiences to maximize both user satisfaction and learning outcomes.

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

Flooding is one of the most destructive natural phenomena that happen worldwide, leading to the damage of property and infrastructure or even the loss of lives. The escalation in the intensity and number of flooding events as a result of the combination of climate change and anthropogenic factors motivates the need to adopt real-time solutions for mapping flood hazards and risks. In this study, a methodological framework is proposed that enables the assessment of flood hazard and risk levels of severity dynamically by fusing optical remote sensing (Sentinel-1) and GIS-based data from the region of the Trieste, Monfalcone and Muggia Municipalities. Explainable machine learning techniques were utilised, aiming to interpret the results for the assessment of flood hazard. The flood inventory was randomly divided into $70\%$, used for training, and $30\%$, employed for testing. Various combinations of the models were evaluated for the assessment of flood hazard. The results revealed that the Random Forest model achieved the highest F1-score (approx. 0.99), among others utilised for generating flood hazard maps. Furthermore, the estimation of the flood risk was achieved by a combination of a rule-based approach to estimate the exposure and vulnerability with the dynamic assessment of flood hazard.

Fall events among older adults are a serious concern, having an impact on their health and well-being. The development of the Internet of Things (IoT) over the last years has led to the emergence of systems able to track abnormal body movements and falls, thus facilitating fall detection and in some cases prevention. Fusing information from multiple unrelated sources is one of the recent trends in healthcare systems. This work aims to provide a survey of recent methods and trends of multisensor data fusion in fall detection systems and discuss their performance, challenges, and limitations. The paper highlights the benefits of developing multimodal systems for fall detection compared to single-sensor approaches, categorizes the different methods applied to this field, and discusses issues and trends for future work.