Vaccination Dashboard Development during COVID-19: A Design Science Research Approach

The COVID-19 pandemic has affected the lives of people worldwide since the beginning of 2020. Since vaccines against COVID-19 have become available, the issue of vaccination has become increasingly important. Accordingly, vaccination dashboards are provided to inform the public about COVID-19 vaccination developments. In our study, we used a design science research (DSR) approach to explore what information vaccination dashboards should provide and how they should be designed. In addition to an initial literature review, we analyzed existing vaccination dashboards and derived information categories. Thereafter, we conducted an online survey to identify the most important metrics from a user’s perspective. Our results indicate that, in addition to vaccination coverage, a comparison of vaccination efficacy and side effects is important. Subsequently, a click prototype was developed and expert interviews were carried out to determine how vaccination dashboards should be designed and which technical issues should be considered

FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

BackgroundForests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset.ResultsThe test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with the given data.ConclusionThe integration of the robust feature extraction of FireXnet with the transparency of explainable AI using SHAP enhances the model’s interpretability and allows for the identification of key characteristics triggering wildfire detections. Extensive experimentation reveals that in addition to being accurate, FireXnet has reduced computational complexity due to considerably fewer training and non-training parameters and has significantly fewer training and testing times

Deep Learning Approach to Improve Spatial Resolution of GOES-17 Wildfire Boundaries using VIIRS Satellite Data

The rising severity and frequency of wildfires in recent years in the U.S. have raised numerous concerns regarding the improvement in wildfire emergency response management and decision-making systems, which require operational high temporal and spatial resolution monitoring capabilities. Satellites are one of the tools that can be used for wildfire monitoring. However, none of the currently available satellites provide both high temporal and spatial resolution. For example, GOES-17 geostationary satellite has a high temporal (5 min) but a low spatial resolution (2 km), and VIIRS polar orbiter satellite has a low temporal (~12 h) but high spatial resolution (375 m). This study aims to leverage currently available satellite data sources, such as GOES and VIIRS, along with Deep Learning (DL) advances to achieve an operational high-resolution wildfire monitoring tool.This study considers the problem of increasing the spatial resolution of low resolution satellite data using high resolution satellite. An Autoencoder DL model is proposed to learn how to map GOES-17 geostationary low spatial resolution satellite images to VIIRS polar orbiter high spatial resolution satellite images. In this context, several loss functions and architectures are implemented and tested to predict both the area of fire and corresponding fire radiance values. These models are trained and tested on wildfire sites from 2019 to 2021 in the western U.S. The results indicate that DL models can improve the spatial resolution of GOES-17 images, leading to images that mimic the spatial resolution of VIIRS images. Combined with GOES-17 higher temporal resolution, the DL model can provide high-resolution near-real-time wildfire monitoring capability as well as semi-continuous wildfire progression maps

Early Forest FIre Detection using UAV and Computer Vision

Τα τελευταία χρόνια, ένα σημαντικό περιβαλλοντικό πρόβλημα με τεράστιες οικονομικές συνέπειες που απασχολεί τις περισσότερες ευρωπαϊκές χώρες είναι οι δασικές πυρκα- γιές. Οι κλασικές τεχνικές αναγνώρισης και αντιμετώπισης είναι ανεπαρκείς με μεγάλες απώλειες να σημειώνονται κάθε χρόνο εξαιτίας τους. Τα τελευταία χρόνια έχουν γίνει πολ- λές προτάσεις στον τομέα της πυρανίχνευσης, με τις περισσότερες από αυτές να είναι πολύ δαπανηρές και τεχνολογικά προηγμένες, με αποτέλεσμα απαιτούν αρκετά μεγάλες τεχνολογικές υποδομές για τη συντήρησή τους. Η λύση που προτείνουμε σε αυτή την έρευνα αφορά την αναγνώριση πυρκαγιάς με χρήση μη επανδρωμένου αεροσκάφους σε συνδυασμό με μηχανική όραση. Πιο συγκεκριμένα, έχουμε εκπαιδεύσει ένα μοντέλο αναγνώρισης αντικειμένων (Yolov5) μέσω ενός προσαρ- μοσμένου συνόλου δεδομένων (εικόνων) πυρκαγιάς. Στη συνέχεια, το drone μας κατά την πτήση χρησιμοποιεί αυτό το συγκεκριμένο μοντέλο για τον εντοπισμό δασικών πυρκα- γιών. Στη συνέχεια, τα δεδομένα που συλλέγουμε από το drone αποστέλλονται μέσω του υπολογιστή σε έξυπνες συσκευές και μέσα από μια εφαρμογή που θα έχουν εγκαταστή- σει οι πυροσβεστικές αρχές στα κινητά τους τηλέφωνα θα μπορούν να δουν αμέσως τον τόπο, την ώρα, την ημερομηνία αλλά και τη φωτογραφία της πυρκαγιάς , προκειμένου να παρέμβουν άμεσα για την ελέγξουν αποτελεσματικά.In recent years, a major environmental problem with huge economic consequences that concern most European countries are forest fires. Classical identification and treatment techniques are found to be insufficient with large losses occurring each year due to them. There have been many proposals in the field of fire detection, with most of them being very costly and technologically advanced, requiring large technological infrastructure to maintain them. The solution we propose in this research concerns fire identification using UAVs combined with computer vision. More specifically, we have trained an object recognition model (Yolov5) through a custom fire dataset (images). The data collected by the drone are sent through the computer to smart devices and through an application that the fire authorities will have installed on their mobile phones, they can immediately see the place, the time, the date and also the photo of the fire, in order to intervene immediately and control it effectively