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.

Over the last decade, the use of Machine/Deep learning algorithms and methodologies has found widespread application across various domains in wildfire science, with fire occurrence risk prediction being one of the extensively covered areas. Many algorithms and architectures have been explored to solve the problem as a binary classification task focusing mainly on the model’s classification performance through the standard classification metrics. However, in the context of an application for predicting the fire occurrence risk we have to enhance the trust in the model’s output by deriving a well defined fire susceptibility index, as well as interpretable insights for the model’s predictions when applied to real-world datasets. In this manuscript we propose model agnostic solutions towards (a) the production of a probability-based binning of the model’s prediction distribution for generating a scalar wildfire occurrence risk and (b) of a representative sampling for producing local prediction explanations spanning the whole dataset, to tackle the slow processing times of explainable AI frameworks, when generating vast amounts of local interpretations. The objective is to contribute the development of a reliable application predicting next day’s fire risk, facilitating the adoption of ML-based solutions by users who are not experts in data science.

Under the framework of MedEWSa project, areas with different climatic and physiographic conditions, which at the same time face similar hazardous events, are coupled to form twins so as to exchange meaningful and long-term knowledge as well as best practices. The first of those twins consists of the Attica Region in Greece and Ethiopia. Both areas need to remediate against a growing frequency and intensity of wildfires. The existing service capabilities need to be improved so as to enhance decision support by first responders and policy makers.In this presentation the existing services as well as the ongoing developments of the project will be demonstrated.

In this paper we explore correlations between soil bacterial diversity and spectral indices derived from satellite remote sensing. We computed alpha and beta diversity indices using metabarcoding data generated from 214 cropland soil samples collected in the context of Eurostat’s 2018 Land Use and Coverage Area frame Survey (LUCAS) Soil module. Subsequently, we derived 13 spectral indices from Sentinel2 images for the sample locations, sticking to the closest available image in relation to the sampling date. We explored the correlations between soil bacterial diversity and spectral indices using univariate Spearman correlations and Mantel tests for beta diversity dissimilarity matrices. We also stratified our analyses in terms of crop type. Significant correlations emerged between alpha and beta diversity and vegetation greenness, soil salinity, moisture, soil texture and chemical content. For beta diversity we found significant correlations only with soil-related Sentinel-2-derived indices. Correlation-based analysis of the commonest crops showed divergent results for bacterial diversity compared to the full cropland sample, and between the crop types.

The continuous increase in global population and the impact of climate change on crop production are expected to affect the food sector significantly. In this context, there is need for timely, large-scale and precise mapping of crops for evidence-based decision making. A key enabler towards this direction are new satellite missions that freely offer big remote sensing data of high spatio-temporal resolution and global coverage. During the previous decade and because of this surge of big Earth observations, deep learning methods have dominated the remote sensing and crop mapping literature. Nevertheless, deep learning models require large amounts of annotated data that are scarce and hard-to-acquire. To address this problem, transfer learning methods can be used to exploit available annotations and enable crop mapping for other regions, crop types and years of inspection. In this work, we have developed and trained a deep learning model for paddy rice detection in South Korea using Sentinel-1 VH time-series. We then fine-tune the model for i) paddy rice detection in France and Spain and ii) barley detection in the Netherlands. Additionally, we propose a modification in the pre-trained weights in order to incorporate extra input features (Sentinel-1 VV). Our approach shows excellent performance when transferring in different areas for the same crop type and rather promising results when transferring in a different area and crop type.

Flood is defined as one of the most devastating natural hazards that lead to immeasurable damages in terms of human settlements and economic losses. As part of the Mediterranean region, Cyprus suffers from this disaster, being subjected to extreme events, land use changes and economic development. In this study, flood hazard is estimated and its extent on residential areas, villages, and agricultural areas located within the Garyllis basin is mapped and analyzed with the integration of remote sensing techniques, GIS, in-situ data, field visits and hydraulic modeling. Open-source HEC-RAS software has been used to estimate the spatial pattern of water surface depths during a 24-hour event for a 1000-year return period. Preliminary results indicate that the areas most susceptible to flooding are observed at the southern part of the basin. The intent is to assist policy makers and planners in the development of flood mitigation measures.

Extreme precipitation events pose a critical threat nowadays, as they can lead to widespread floods and trigger intense landslide phenomena. Moreover, the resulting extended floods can subsequently cause additional landslide events. This occurs because floods have the potential to significantly alter the landscape of a region, rendering it, in some cases, more susceptible to landslides than before. This alteration highlights the importance of promptly conducting an updated landslide susceptibility assessment (LSA) after such events to ensure the safety of the local population and the stability of critical infrastructure. However, it also complicates the LSA due to the changes it causes in many landslides causal factors. Geographical Information System (GIS) and Remote Sensing (RS) techniques can contribute significantly by providing updated information, even over a wide area. In this direction, the extreme precipitation events that occurred in February 2019 in Chania Prefecture, Crete-Island, Greece, are examined as a case study to highlight the potentiality of GIS and RS.

The rapid growth of Earth Observation (EO) and Remote Sensing (RS) data has underscored the critical need for identifying optimal solutions to effectively manage EO Big Data. This entails simplifying data sharing and facilitating adaptation across multidisciplinary applications to better serve the research community. Various architectures and structures have been developed to manage and deploy these data in an analysis-ready format. In this study, we provide a concise overview of an advanced EO Big Data infrastructure located in Limassol, Cyprus, comprising diverse data sources acquired from an acquisition station, an atmospheric ground base station, and various living labs. Additionally, we present the EO data ecosystem of Cyprus that is specifically designed to efficiently store the aforementioned data.

A new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth observation data with a particular focus on mosquito abundance (MA) estimation, is proposed here. We leverage the Variational AutoEncoder architecture to derive a confidence metric by the latent space representations of Earth observation datasets. This methodology is pivotal in establishing a correlation between the Euclidean distance in latent representations and the absolute error in individual MA predictions. Our study focuses on Earth observation datasets from the Veneto region in Italy and the Upper Rhine Valley in Germany, considering areas significantly affected by mosquito populations. A key finding is a notable correlation of 0.46 between the absolute error of MA predictions and the proposed confidence metric. This correlation signifies a robust, new metric for quantifying the reliability and enhancing the trustworthiness of the AI/ML model predictions in the context of both Earth observation data analysis and mosquito abundance studies.

This study develops a Data-Driven Machine Learning (ML) pipeline to predict West Nile Virus (WNV) outbreak risk at NUTS31level for every month of the transmission period (May - October), utilizing Earth Observation (EO), Socioeconomical, and Entomological data. While most related works predict WNV annually, we assess the feasibility of a per-month prediction approach using the Binary Relevance (BR) method as a baseline and comparing it to the Classifier Chain (CC) approach. Testing on four regions in Greece namely Attica, Central Macedonia, Thrace, and Thessaly reveals that the CC method outperforms the BR method, achieving an F1Score of 0.55. Our results show that a fine temporal resolution is viable when individual predictions are treated as a sequence and linked using a chained classifier, demonstrating the effectiveness of the chained approach.

This study examines the long-term dynamics of the Paphos forest in Cyprus using Landsat satellite data for Vegetation Indices (VIs), MODIS data for evapotranspiration, and CHIRPS data for precipitation from 1991 to 2022. Sen's slope method was applied to analyse the trends in the data, revealing statistically significant positive trends in the vegetation indices despite the nearly constant precipitation, indicating increased forest vegetation over the past 30 years. Scatterplots were created mainly to examine correlations within the VIs and precipitation data but with low R-squared values ranging between 0.15-0.44. The study outcomes highlight a complex relationship with evapotranspiration and a weak correlation between precipitation and vegetation indices. These findings could be essential in understanding how forests work, especially in a semi-arid environment like Cyprus.

This paper introduces an original application for detecting changes related to diverse agricultural activities through the analysis of bitemporal Sentinel-2 satellite imagery. Operating without pre-existing samples, our approach generates pseudo-labels using common rule-based Earth Observation (EO) algorithms to identify cases of abrupt loss of vegetation in pairs of consecutive cloud-free images. These artificially generated samples form the basis for training several state-of-the-art change detection (CD) methods. Evaluation on a small ground truth sample, annotated through photo-interpretation by experts, demonstrates our semi-supervised methodology’s high predictive accuracy for agricultural events detection across diverse terrains and cropping practices (i.e., mowing, grazing, harvest, plowing, stubble burning, etc.). The proposed implementation offers a cost-effective, scalable solution for real-time monitoring, providing valuable insights for agricultural activity and facilitating informed decision-making in farm management and biodiversity strategies.

This work illustrates an integrated methodology for wildfire vulnerability assessment in selected Wildland Urban Interface (WUI) areas, leveraging Web Services, Remote Sensing, Google Earth Engine (GEE) and GIS techniques. The study generates and fuses physical vulnerability factors (fuel, canopy density, DEM derivatives, and remote sensing indices) and socio-economic elements (population age and density, building materials, land values, and Points of Interest). Thenceforth, fire vulnerability maps were created in a GIS environment incorporating data for the most intense fire period recorded in the past 38 years (1984-2021) in the Attica region. Key municipalities that were investigated include Markopoulo Mesogaias, Lavreotiki, Saronikos, Oropos, and Acharnon. As for the findings, they highlight the need for targeted mitigation and community resilience, aiding in identifying vulnerable regions and managing wildfires in Attica's WUI areas.

In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. Even though recent advancements in AI-driven digital agriculture can offer high-performing predictive functionalities, they lack tangible quantitative evidence on their benefits to the farmers. Field experiments can derive such evidence, but are often costly, time consuming and hence limited in scope and scale of application. To this end, we propose an observational causal inference framework for the empirical evaluation of the impact of digital tools on target farm performance indicators (e.g., yield in this case). This way, we can increase farmers' trust via enhancing the transparency of the digital agriculture market, and in turn accelerate the adoption of technologies that aim to secure farmer income resilience and global agricultural sustainability against a changing climate. As a case study, we designed and implemented a recommendation system for the optimal sowing time of cotton based on numerical weather predictions, which was used by a farmers' cooperative during the growing season of 2021. We then leverage agricultural knowledge, collected yield data, and environmental information to develop a causal graph of the farm system. Using the back-door criterion, we identify the impact of sowing recommendations on the yield and subsequently estimate it using linear regression, matching, inverse propensity score weighting and meta-learners. The results revealed that a field sown according to our recommendations exhibited a statistically significant yield increase that ranged from 12% to 17%, depending on the method. The effect estimates were robust, as indicated by the agreement among the estimation methods and four successful refutation tests. We argue that this approach can be implemented for decision support systems of other fields, extending their evaluation beyond a performance assessment of internal functionalities.

In this paper, we handle the problem of next day wildfire prediction via the use of machine learning. In contrast to most works in the relevant literature, we set the problem to its realistic basis, with respect to its large scale, the extreme imbalance in the data distribution, the required high spatial granularity of the predictions and the consideration of the strong spatial correlations inherent in the data. We implement a machine learning workflow that exploits Tree Ensemble and Neural Network algorithms, upon which an extensive hyperparameter search procedure is performed, via cross-validation, in order to select a set of effective models that are expected to generalize well on new data. Our experiments on the whole Greek territory demonstrate the effectiveness of the proposed methodology, rendering it directly applicable to real-world scenarios. Finally, several insights towards further improving the effectiveness of current models are discussed.

This study presents a dynamic phenology stage estimation methodology for cotton towards early warning and mitigation advice against natural disasters. First, a time-series comparison algorithm, based on Earth Observation (EO) data, is used to assign pseudo-labels to approximately 1,000 parcels. For this, we employ only a limited number of ground truth samples. The pseudo-labels are then used to train Random Forest (RF) regression models for phenology stage estimation. The pseudo-labeling process is used to augment the annotated dataset and allow for modelling the growth of cotton. The models are applied and evaluated on two different test sites in Greece; for which field campaigns were carried out to collect the labels. The results are satisfactory and showcase the successful generalization of the models to other areas. The dynamic predictions for cotton growth and extreme weather events, from numerical weather prediction (NWP) models, are invaluable information for decision-making relevant to agricultural insurance schemes and farm management.

We present a real-time wildfire monitoring service that exploits satellite images and linked geospatial data to detect hotspots and monitor the evolution of fire fronts. The service makes heavy use of scientific database technologies (array databases, SciQL, data vaults) and linked data technologies (ontologies, linked geospatial data, stSPARQL) and is implemented on top of MonetDB and Strabon. The service is now operational at the National Observatory of Athens and has been used during the previous summer by emergency managers monitoring wildfires in Greece.

TELEIOS is a recent European project that addresses the need for scalable access to petabytes of Earth Observation data and the discovery and exploitation of knowledge that is hidden in them. TELEIOS builds on scientific database technologies (array databases, SciQL, data vaults) and Semantic Web technologies (stRDF and stSPARQL) implemented on top of a state of the art column store database system (MonetDB). We demonstrate a first prototype of the TELEIOS Virtual Earth Observatory (VEO) architecture, using a forest fire monitoring application as example.

The Permanent Scatterers (PS) technique, invented by Politechnico di Milano research team, is an approach that minimises the undesirable noise components in the classic InSAR technique, such as spatial and temporal decorrelations, signal delay due to tropospheric and ionospheric disturbances, orbital errors as well as topographical errors. This approach is suitable for the measurement of near vertical displacements of the order of ∼1 mm per year. It exploits almost all of the available SAR interferometric data over an area and requires availability of natural and/or artificial permanent scatterers. In this study we describe the implementation of the PS technique, called PerSePHONE (Permanent Scatterers Project Held by the Observatory, National, of Hellas). Its development has been based on a number of algorithmic adaptations, as well as new approaches in PS candidate selection. An example of this implementation is shown for the case of the Corinth Rift area (Hellas).

This paper describes a knowledge-based system which has been developed for integrating easily-available geographical context information from a GIS in remotely-sensed image analysis. An experiment is described in which soil maps and buffered road networks have been used as additional data layers for classifying single date SPOT images for estimates of crop acreages. The map datasets have been digitised, co-registered to the satellite imagery, and manipulated using ARC/INFO. The knowledge base consists of both image context rules and geographical context rules. Probabilistic information from the image classifier and from the rule base is combined using the Dempster-Shafer model of evidential reasoning. Tests using ground data from the Departement Loir-et-Cher, France, have shown that use of the knowledge-based system with GIS data gives an accuracy improvement of approximately 13 per cent compared to using a parametric image classifier alone.