Skip to content
Log in
Sponsored Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt Design Multi-Agent AI Systems Using MCP and A2A: Engineer your own Pyt
#artificial intelligence Review Open access

A Pan-European Multimodal Dataset Linking Field Photos with Satellite Image Time Series for Agricultural Mapping

M. F. Celik Stefano Maurogiovanni Rocco Sedona Gabriele Cavallaro V. Marsocci Ruben Cartuyvels Momchil Yordanov M. van der Velde Claudia Paris
Sep 2026 · Scientific Data · 0 citations

Abstract

AgroFusion is a continental-scale, multimodal, pixel-based reference dataset designed to support rigorous evaluation and reproducible research in agricultural remote sensing and geospatial Artificial Intelligence (AI). Built upon the 2018 and 2022 Land Use/Cover Area frame statistical Survey (LUCAS), the dataset integrates 211,483 field observations (107,642 in 2018 and 103,841 in 2022), each linked to field photos (four cardinal views, point centered view, and a close-up), LUCAS land-cover and land-use annotations, with monthly pixel-based Satellite Image Time Series (SITS) from Sentinel-2, Sentinel-1, and Landsat-8, agrometeorological data from ERA5-Land, topography from the Copernicus GLO-30 DEM, soil properties from AI4SoilHealth, and annual embeddings from AlphaEarth geo-foundation model. AgroFusion is the first continental-scale resource for pairing geo-tagged field photos with pixel-based SITS, semantically rich agricultural covariates, and precomputed foundation model embeddings. This combination will enable a wide range of applications, including multimodal and multiscale crop type classification and phenology retrieval, and the interpretation of satellite-derived agricultural products using ground-level visual context.

DOI Semantic Scholar
Read PDF

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Feb 2024

KTO: Model Alignment as Prospect Theoretic Optimization

This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.

Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff et al. · 195 citations · ⚡28
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Nov 2024

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...

Masoud Mohseni, Artur Scherer, K. Johnson et al. · 121 citations · ⚡9
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10
#artificial intelligence Aug 2024

The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models

This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.

Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al. · 109 citations · ⚡19

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.