15 citations · 36 across the 18 of their papers we have counts for
20 papers
Deep Learning Super Resolution for Satellite Cloud Mask Downscaling
Angelos Georgakis, Valentina Kanaki, Giorgos Giannopoulos +4
A vast amount of optical satellite data is being transmitted to Earth-based servers every day, and more than half of this data is affected by haze or clouds. Additionally, this dat…
Space2Ground 2.0: A Multi-Source Dataset and Framework for Agricultural Monitoring through Fusion of Street-Level and Satellite Imagery
Iason Tsardanidis, Alkiviadis Koukos, George Choumos +2
Accurate and scalable parcel-level agricultural monitoring remains challenging because satellite Earth Observation alone provides only an overhead perspective of agricultural parce…
Comparative Assessment of Multimodal Earth Observation Data for Soil Moisture Estimation
Ioannis Kontogiorgakis, Athanasios Askitopoulos, Iason Tsardanidis +4
Accurate soil moisture (SM) estimation is critical for precision agriculture, water resources management and climate monitoring. Yet, existing satellite SM products are too coarse…
Positive-Unlabeled Learning for Control Group Construction in Observational Causal Inference
Ilias Tsoumas, Dimitrios Bormpoudakis, Vasileios Sitokonstantinou +4
In causal inference, whether through randomized controlled trials or observational studies, access to both treated and control units is essential for estimating the effect of a tre…
Monitoring digestate application on agricultural crops using Sentinel-2 Satellite imagery
Andreas Kalogeras, Dimitrios Bormpoudakis, Iason Tsardanidis +2
The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satelli…
Mapping of Weed Management Methods in Orchards using Sentinel-2 and PlanetScope Data
Ioannis Kontogiorgakis, Iason Tsardanidis, Dimitrios Bormpoudakis +5
Effective weed management is crucial for improving agricultural productivity, as weeds compete with crops for vital resources like nutrients and water. Accurate maps of weed manage…
Next day fire prediction via semantic segmentation
Konstantinos Alexis, Stella Girtsou, Alexis Apostolakis +2
In this paper we present a deep learning pipeline for next day fire prediction. The next day fire prediction task consists in learning models that receive as input the available in…
Cloud gap-filling with deep learning for improved grassland monitoring
Iason Tsardanidis, Alkiviadis Koukos, Vasileios Sitokonstantinou +2
Uninterrupted optical image time series are crucial for the timely monitoring of agricultural land changes, particularly in grasslands. However, the continuity of such time series…
A Latent Space Metric for Enhancing Prediction Confidence in Earth Observation Data
Ioannis Pitsiorlas, Argyro Tsantalidou, George Arvanitakis +2
This study presents a new approach for estimating confidence in machine learning model predictions, specifically in regression tasks utilizing Earth Observation (EO) data, with a p…
Causality and Explainability for Trustworthy Integrated Pest Management
Ilias Tsoumas, Vasileios Sitokonstantinou, Georgios Giannarakis +5
Pesticides serve as a common tool in agricultural pest control but significantly contribute to the climate crisis. To combat this, Integrated Pest Management (IPM) stands as a clim…
Understanding the impacts of crop diversification in the context of climate change: a machine learning approach
Georgios Giannarakis, Ilias Tsoumas, Stelios Neophytides +3
The concept of sustainable intensification in agriculture necessitates the implementation of management practices that prioritize sustainability without compromising productivity.…
Evaluating Digital Agriculture Recommendations with Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou +5
In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of smart farming tools. While AI-driven digital agriculture tools can offer hig…
Fuzzy clustering for the within-season estimation of cotton phenology
Vasileios Sitokonstantinou, Alkiviadis Koukos, Ilias Tsoumas +3
Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation,…
Towards Global Crop Maps with Transfer Learning
Hyun-Woo Jo, Alkiviadis Koukos, Vasileios Sitokonstantinou +2
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 nee…
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference
Ilias Tsoumas, Georgios Giannarakis, Vasileios Sitokonstantinou +5
In contrast to the rapid digitalization of several industries, agriculture suffers from low adoption of climate-smart farming tools. Even though AI-driven digital agriculture can o…
Personalizing Sustainable Agriculture with Causal Machine Learning
Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla +1
To fight climate change and accommodate the increasing population, global crop production has to be strengthened. To achieve the "sustainable intensification" of agriculture, trans…
A Data Cube of Big Satellite Image Time-Series for Agriculture Monitoring
Thanassis Drivas, Vasileios Sitokonstantinou, Iason Tsardanidis +3
The modernization of the Common Agricultural Policy (CAP) requires the large scale and frequent monitoring of agricultural land. Towards this direction, the free and open satellite…
Pest presence prediction using interpretable machine learning
Ornela Nanushi, Vasileios Sitokonstantinou, Ilias Tsoumas +1
Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the ins…
Towards Space-to-Ground Data Availability for Agriculture Monitoring
George Choumos, Alkiviadis Koukos, Vasileios Sitokonstantinou +1
The recent advances in machine learning and the availability of free and open big Earth data (e.g., Sentinel missions), which cover large areas with high spatial and temporal resol…
Towards assessing agricultural land suitability with causal machine learning
Georgios Giannarakis, Vasileios Sitokonstantinou, Roxanne Suzette Lorilla +1
Understanding the suitability of agricultural land for applying specific management practices is of great importance for sustainable and resilient agriculture against climate chang…