15 citations · 42 across the 20 of their papers we have counts for
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SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
Maria Gonzalez-Calabuig, Kai-Hendrik Cohrs, Vishal Nedungadi +7
Geospatial foundation models (GFMs) for Earth observation often fail to perform reliably in environments underrepresented during pretraining. We introduce SHRUG-FM, a framework for…
Feature Extraction in the Remote Sensing Data Value Chain: A Systematic Review of Methods and Applications
Nathan Mankovich, Kai-Hendrik Cohrs, Homer Durand +3
Earth observation involves collecting, analyzing, and processing an ever-growing mass of data. This planetary data is crucial for addressing relevant societal, economic, and enviro…
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…