Zuo et al. (2026) Hallucination-Resistant Change Detection in Multimodal Large Models for Autonomous Land Management Agents
Identification
- Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Year: 2026
- Date: 2026-01-01
- Authors: Luo Zuo, Jiayi Sun, Jie Li, Feixiang Liu, Jinglei Li, Guanchong Niu
- DOI: 10.1109/jstars.2026.3656403
Research Groups
[Not available in the provided text.]
Short Summary
This paper investigates methods for achieving hallucination-resistant change detection using multimodal large models, specifically tailored for autonomous land management agents.
Objective
- To develop and evaluate techniques for robust, hallucination-resistant change detection in multimodal large models, applied in the context of autonomous land management.
Study Configuration
- Spatial Scale: [Not specified in the provided text, but implied to be relevant to land areas.]
- Temporal Scale: [Not specified in the provided text, but implied to involve monitoring changes over time.]
Methodology and Data
- Models used: Multimodal Large Models (specific architectures or frameworks are not detailed).
- Data sources: [Not specified in the provided text, but implied to be multimodal data relevant to land management.]
Main Results
- [Not available in the provided text.]
Contributions
- [Not available in the provided text, but likely involves advancing the reliability and accuracy of change detection in complex AI systems for real-world applications.]
Funding
- [Not available in the provided text.]
Citation
@article{Zuo2026HallucinationResistant,
author = {Zuo, Luo and Sun, Jiayi and Li, Jie and Liu, Feixiang and Li, Jinglei and Niu, Guanchong},
title = {Hallucination-Resistant Change Detection in Multimodal Large Models for Autonomous Land Management Agents},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year = {2026},
doi = {10.1109/jstars.2026.3656403},
url = {https://doi.org/10.1109/jstars.2026.3656403}
}
Original Source: https://doi.org/10.1109/jstars.2026.3656403