Kouris dam is the main water reservoir in Cyprus, located in Limassol, transferring water from the western part of Troodos mountains to the eastern part of the island, both for domestic and irrigation use. It is a 113 m high zoned earthfill dam with a central clay core. Since its construction, in 1988, it has overflown three times in 2004, 2012 and 2020. Therefore, the need for its continuous inspection is of great importance for safety reasons. For the detection of surface deformation in Kouris dam, the Persistent Scatterers Interferometry technique was applied on 167 Sentinel-1 images from 2015 to 2023. The InSAR time-series processing revealed small-scale displacements on the downstream slope of the dam with a maximum rate of -5mm/y. The LOS displacements were validated by geodetic measurements, provided by the Water Development Department of Cyprus. This can serve as a valuable tool for risk assessment and mitigation.

CyCLOPS is the only Tier-1 permanent GNSS station network in Cyprus and it is currently comprised of GNSS CORS, co-located with precise weather stations, tilt meters and calibration-grade InSAR corner reflectors. The main objective of this strategic research infrastructure unit was to precisely monitor geohazards in Cyprus and the broader EMMENA region and to render Cyprus a dedicated SAR calibration and validation site. To date, CyCLOPS has successfully estimated very precise positions and velocities of national CORS stations, highlighted the geodynamic regime of the area, and monitored very dynamic landslides occurring throughout the island. CyCLOPS+ is the expansion phase, which will form the Cyprus Ground Motion Service (CyGMS), a continuously updated national velocity model, aiming to deliver precise and reliable ground displacement metrics, and densify the European Ground Motion System, thereby improving the country's resilience to seismic and geological threats.

This paper explores how the Eratosthenes CoE that has been established in Cyprus through the EXCELSIOR H2020 Teaming project can be a hub for Earth Observation activities in Cyprus and the Eastern Mediterranean, Middle East and North Africa (EMMENA) region. Due to its geographical proximity, EXCELSIOR can become a hub for partners in Middle Eastern and Northern African countries. Cyprus' unique geostrategic position can support Earth Observation from satellite programmes in three continents and provide valuable services in the processes of satellite calibration and validation. Finally, the distinct needs and opportunities that motivate the establishment of an Earth Observation Centre of Excellence in Cyprus are presented in this paper.

Oil spills are a major source of marine pollution affecting the environment, economy, and marine ecosystems. Toxic chemicals from oil spills can remain in the ocean for years and even sink to the seabed, affecting sedimentation rates. Although many oil spills are caused by accident, some are caused intentionally by cargo ships dumping waste oil and bilge water. It is very difficult to locate, detect and remove oil from the ocean surface. However, regular monitoring can help prevent illegal dumping and aid remediation efforts. This work aims to detect oil spills in the North-Eastern part of Cyprus using a deep learning model. The results are compared with a conventional Adaptive Thresholding Algorithm. The comparisons demonstrate that the deep learning model has higher accuracy than the adaptive thresholding algorithm.

The geological, ecological, and biological ecosystems of the planet have changed because of the global climate crisis, and this poses a serious threat to humanity as well as the conservation of agricultural productivity and food security. The European Commission outlined the continent's objective to become climate neutral by 2050 with zero net Greenhouse Gas (GHG) emissions. Soil organic carbon (SOC) is closely related to soil quality and has a significant impact on how soil and plants interact. SOC monitoring gives a unique role in agricultural sustainability thus precise prediction and monitoring of SOC is essential. Remote Sensing (RS) evolution, big data accessibility and Deep Learning (DL) architectures present enormous potential for extensive SOC monitoring. Several RS applications (e.g., Sentinels, MODIS, Landsat etc.) along with machine learning and DL methodologies (e.g., RF, ANN, CNN etc.) used in literature for SOC prediction. The current review paper emphasizes on the latest RS approaches used for SOC monitoring.

Wildfires detection is a major issue for authorities. There are various causes of fire events with the most common being human influence. A fire risk prediction model through the analysis of geo-environmental and climate data is important for early warning and fire management. In this work, a dataset from multiple modalities, including road density, travelers, forest-agriculture interface, burned areas from historical fire events, metrological data, land cover, vegetation indices from data cube, is generated. Artificial intelligence and machine learning models can use this multimodal dataset to improve forest fire management.

This paper aims to introduce the concept and objectives of the recently funded AI-OBSERVER Horizon Europe Twinning project titled “Enhancing Earth Observation capabilities of the Eratosthenes Centre of Excellence on Disaster Risk Reduction through Artificial Intelligence”. The AIOBSERVER project aims to significantly strengthen and stimulate the scientific excellence and innovation capacity of the ERATOSTHENES Centre of Excellence on the use of Artificial Intelligence for Earth Observation in the Disaster Risk Reduction thematic area, as well as the research management and administrative skills, of the Centre. This will be achieved through a series of capacity building and targeted research activities, having the support of internationally leading institutions, i.e., the German Research Centre for Artificial Intelligence from Germany and the University of Rome Tor Vergata from Italy, assisting the ERATOSTHENES Centre of Excellence to reach its longterm objective of raised excellence on Artificial Intelligence for Earth Observation on environmental hazards.

Landslides are a significant geohazard with global implications, causing fatalities, infrastructure damage, and economic consequences. Cyprus, located over the Mediterranean fault zone in the Eastern Mediterranean region, experiences active landslides and slope instabilities due to its unique geodynamic regime and seismic activity. Monitoring ground displacements caused by landslides is crucial for early detection and response and can be performed using many techniques like ground-based or satellite-based geodetic, photogrammetric, and Earth Observation (EO)- based techniques. The study focuses on utilizing the Google Earth Engine (GEE) platform and Sentinel-1 and Sentinel-2 satellite imagery to rapidly map landslides in the Paphos District of Cyprus. Multitemporal SAR change detection using Sentinel-1 images and spectral indices from Sentinel-2 data were employed. The results were validated using highresolution images from Google Earth and data from the Geological Survey Department. The study successfully identified landslide areas, confirmed by ground truthing, and provided insights into the timing of landslide occurrences.