Engineering Proceedings

ITISE-2025 (11th International conference on Time Series and Forecasting) seeks to provide a forum for scientists, engineers, educators, and students to discuss the latest ideas and realizations in the foundations, theory, and models of and applications for interdisciplinary and multidisciplinary research encompassing disciplines of computer science, mathematics, statistics, forecaster, econometric, etc [...]

This study proposes a forest mapping framework for Algeria that addresses the challenge of limited clean data and noisy global land cover labels. The approach combines a small set of manually curated annotations with noisy ESA WorldCover data, leveraging Sentinel-2 multispectral imagery and Digital Elevation Model (DEM) features such as slope, aspect, and the Normalized Difference Vegetation Index (NDVI). A modified ResNet-18 architecture was fine-tuned using both clean and pseudo-labeled noisy data, enabling the model to effectively mitigate label noise. The framework achieved an overall accuracy of 98.5%, demonstrating strong generalization across Algeria’s diverse forest ecosystems. These results highlight the potential of semi-supervised deep learning to improve large-scale forest monitoring, with applications in conservation, sustainable resource management, and climate change mitigation.

It is well-known that GNSS high accuracy solutions are increasingly vulnerable to jamming and spoofing attacks, posing significant challenges to their reliability, security, and accuracy. In the past years, GNSS communities have witnessed an increase in the frequency and sophistication of these attacks, driven, among other factors, by the widespread availability of low-cost, off-the-shelf equipment capable of denying or even totally misleading GNSS-based positioning systems. On the one hand, jamming attacks aim at inhibiting signal reception by introducing high-power noise or interference, leading to degraded performance or complete failure in determining position. Jamming detection mechanisms need to be traced to GNSS receiver mitigation measures at signal processing level to analyze the radio frequency (RF) environment or receiver behavior. Signal-to-noise ratio (SNR) monitoring, power spectrum analysis, and signal power monitoring are commonly used to detect anomalies in signal characteristics. Jamming is often indicated with the presence of a combination of one or more dedicated indicators, opening space to characterize different levels of jamming attack allowing to optimize a response at user level. On the other hand, detecting spoofing attacks requires different advanced techniques to identify anomalies in satellite signals, receiver behavior, or consistency of computed position data. Indicators regarding internal consistency checks, as well as unexpected evolutions of GNSS signals, are typically suspicious behaviors to be analyzed as possible attacks. Additionally, ensuring trust in the received navigation information by including cryptographic authentication mechanisms is key to quickly detecting some kinds of spoofing. This paper presents the latest enhancements on jamming and spoofing detection and mitigation mechanisms for GMV GSharp^® high accuracy and safe positioning solution. This new method, based on fuzzy logic systems, allows us to distinguish between different levels of attack and adapt the reactions to reduce the impact on the final user as much as possible. Additionally, test results obtained from real GNSS attacks datasets will be shown.