Psoriatic Arthritis (PsA) is a chronic, inflammatory disease affecting joints, substantially impacting patients' quality of life, with European guidelines for managing PsA emphasizing the importance of assessing hand function. Here, we present a set of novel digital biomarkers (dBMs) derived from a touchscreen-based serious game approach, DaktylAct, intended as a proxy, gamified, objective assessment of hand impairment, with emphasis on fine motor skills, caused by PsA. This is achieved by its design, where the user controls a cannon to aim at and hit targets using two finger pinch-in/out and wrist rotation gestures. In-game metrics (targets hit and score) and statistical features (mean, standard deviation) of gameplay actions (duration of gestures, applied pressure, and wrist rotation angle) produced during gameplay serve as informative dBMs. DaktylAct was tested on a cohort comprising 16 clinically verified PsA patients and nine healthy controls (HC). Correlation analysis demonstrated a positive correlation between average pinch-in duration and disease activity (DA) and a negative correlation between standard deviation of applied pressure during wrist rotation and joint inflammation. Logistic regression models achieved 83% and 91% classification performance discriminating HC from PsA patients with low DA (LDA) and PsA patients with and without joint inflammation, respectively. Results presented here are promising and create a proof-of-concept, paving the way for further validation in larger cohorts.

Social media data have been widely used in disaster management and particularly for the early detection of disaster emergencies. However, apart from sudden crises, there are also creeping crises, which are less evident but can be equally threatening to human lives, such as water pollution. The question raised is whether social media data can be used for discovering issues of water quality. In this work we attempt to answer this question by collecting posts from Twitter during the period of one year, which contain keywords about water quality, and applying three well-known techniques for event detection, i.e. Z-score, STA/LTA, and DBSCAN. A detailed presentation of the detected events, both relevant and not relevant, is given to provide more insight and proves that it is indeed feasible to identify water quality events with social media data. In addition, a quantitative evaluation of the three methods, in terms of precision, shows the superiority of Z-score for this particular topic.

The increase of social media use in recent years has shown potential also for the identification of specific trends in the data that could be used to locate earthquakes. In this work, we implemented a pipeline that uses Twitter data to identify locations of earthquakes and use the information to trigger EO data analysis. We tested the pipeline for almost a year over Japan, an area where earthquake events are frequent, as well as the use of social media in the population. Here, we show the results and discuss the potential development of such procedures. In the future, considering the rapid development and the increase of satellite constellations aimed at global coverage with short revisit times, algorithms of this kind could be used to prioritize satellite acquisitions for the detection of the areas most affected by earthquake damages.

Recent developments in remote sensing have shown that snow depth can be estimated accurately on a global scale using satellite images through cross-polarization and copolarization backscatter measurements. This method does, however, have some limitations in low-land areas with dense forest coverage and shallow snow, which are often found nearby urban areas. In these areas, citizen observations can be fused with satellite-based estimations to deliver more accurate solutions. To that end, we use snow-related tweets that have been annotated by artificial intelligence (AI) methods and are introduced in a novel neural network model, aiming to increase the estimation accuracy of the state-of-the-art remote sensing method. The proposed model combines the estimated snow depth from Sentinel 1 images with the number of Twitter posts and Twitter images that are semantically relevant to snow. The use of instant social media data for purposes of snow depth estimation is investigated, validated, and tested in Finland. Our results show that this approach does improve the snow depth estimation, highlighting its potential for use in civil protection agencies in managing snow conditions.

Artificial Intelligence (AI) is already part of our lives and is extensively entering the space sector to offer value-added Earth Observation (EO) products and services. The Copernicus programme provides data on a free, full and open basis, while the recently launched Data and Information Access Service (DIAS) providers index, store and exchange tremendous amounts of data and cloud infrastructure computational resources. Copernicus data and other georeferenced data sources are often highly heterogeneous, distributed and semantically fragmented. One example is the massively generated social media data from citizen observations, including visual, textual and spatiotemporal information. Social media information offers reliable, timely and very prescriptive information about a crisis event. In this work we present the multimodal fusion aspects for combining satellite images and social media for emergency response, such as flood monitoring and extreme weather conditions in polar regions.

This work aims to advance Web Service retrieval, also known as Matching, in two directions. First, it introduces a matching algorithm for SAWSDL, which adapts and extends known concepts with novel strategies. Effective logic-based and syntactic strategies are introduced and combined in a novel hybrid strategy, targeting an envisioned well-defined, real-world scenario for matching. The algorithm is evaluated in a universal environment for matching algorithms, SME2, in an objective, reproducible manner. Evaluation ranks Tomaco high amongst state of the art, especially for early recall levels (first in macro-averaging precision, up to 0.7 recall). Secondly, this work introduces the Tomaco web application, which aims to promote wide-spread adoption of Semantic Web Services while targeting the lack of user-friendly applications in this field, by integrating a variety of configurable matching algorithms proposed in this paper. It, finally, allows discovery of both existing and user-contributed service collections and ontologies, serving also as a service registry.