ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Geoinformatics
This article is part of the Research TopicArtificial Intelligence in Remote Sensing and Geosciences: Applications for Earth ObservationsView all 3 articles
A Hybrid Deep Learning Framework for Multi-Source Data Fusion and Super-Resolution Mapping in AI-Powered Earth Observation Applications
Provisionally accepted
- Nanjing Normal University, Nanjing, China
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Achieving ethical and sustainable decision-making in agri-food systems necessitates computational methods that integrate empirical data with normative and environmental constraints. Existing evidence synthesis approaches often fail to capture the intricate relationships among ecological integrity, social equity, and economic feasibility. To address these challenges, we propose a machine learning-enhanced framework comprising three core components: a symbolic formalization layer for multi-agent ethical modeling, a constraint-aligned architecture (EthosNet), and an optimization strategy (AgriDualAlign) that aligns learned policies with ethical priors. Experimental evaluations on four benchmark datasets demonstrate that our method consistently outperforms strong multimodal baselines such as CLIP, ViLT, and OpenFlamingo. Our model achieves a 20% improvement in ethical constraint compliance (reducing violation rate to 6.1%), a 3.5-point gain in F1 score on sustainability classification, and a 5.2-point increase in AUC on ethical decision-making tasks. These results confirm the framework's superior interpretability, ethical robustness, and generalization ability across diverse agri-food scenarios.
Keywords: Classification Accuracy, cross-attention fusion module, domain-adaptive backbone, Earth Observation, Hybrid deep learning, Multi-source data fusion, spatial resolution, Super-resolution mapping
Received: 21 Aug 2025; Accepted: 05 Feb 2026.
Copyright: © 2026 Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ming Deng
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