From the 1 of 6 linked papers with an AI index.
6 papers
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 +3
The paper introduces Space2Ground 2.0, a framework that combines satellite SAR/multispectral data with crowdsourced street‑level photos to create a parcel‑level agricultural monito…
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…
Staged Event Trees for Transparent Treatment Effect Estimation
Gherardo Varando, Manuele Leonelli, Jordi Cerdà -Bautista +2
Average and conditional treatment effects are fundamental causal quantities used to evaluate the effectiveness of treatments in various critical applications, including clinical se…
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…
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…