15 citations · 36 across the 18 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…