AIGC时代下遥感新闻权威性的反思与风险治理研究

张新长; 齐霁; 时天东; 崔浩

doi:10.13203/j.whugis20260316

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AIGC时代下遥感新闻权威性的反思与风险治理研究

摄影测量与遥感 | 更新时间：2026-11-05

- AIGC时代下遥感新闻权威性的反思与风险治理研究
- Rethinking the Authority of Remote Sensing News and Its Risk Governance in the AIGC Era
- 武汉大学学报（信息科学版） 2026年51卷第10期页码：2124-2134
- 作者机构：
  
  1.广州大学地理科学与遥感学院，广东广州，510006
  2.新疆大学地理与遥感科学学院，新疆乌鲁木齐，830017
  3.中南大学地球科学与信息物理学院，湖南长沙，410083
  4.武汉大学测绘遥感信息工程全国重点实验室，湖北武汉，430079
- 作者简介：
  
  张新长，博士，教授，主要从事空间数据整合及自适应更新技术方法、数字城市(智慧城市)理论与方法、深度学习与自然资源要素分类和提取等方面的教学与研究工作。zhangxc@gzhu.edu.cn
  齐霁，博士。jameschi95@foxmail.com
- 基金信息：
  
  国家自然科学基金(42571533;42371406);空间基准全国重点实验室开放基金(SKLSD2026-KF-27)
- DOI：10.13203/j.whugis20260316
  中图分类号： P237
- 收稿：2026-09-20，
  
  纸质出版：2026-10-05
- 稿件说明：
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张新长,齐霁,时天东，等.AIGC时代下遥感新闻权威性的反思与风险治理研究[J].武汉大学学报（信息科学版）,2026,51(10):2124-2134.

ZHANG Xinchang,QI Ji,SHI Tiandong,et al.Rethinking the Authority of Remote Sensing News and Its Risk Governance in the AIGC Era[J].Geomatics and Information Science of Wuhan University,2026,51(10):2124-2134.
张新长,齐霁,时天东，等.AIGC时代下遥感新闻权威性的反思与风险治理研究[J].武汉大学学报（信息科学版）,2026,51(10):2124-2134. DOI：10.13203/j.whugis20260316

ZHANG Xinchang,QI Ji,SHI Tiandong,et al.Rethinking the Authority of Remote Sensing News and Its Risk Governance in the AIGC Era[J].Geomatics and Information Science of Wuhan University,2026,51(10):2124-2134. DOI：10.13203/j.whugis20260316

摘要

随着生成式人工智能（artificial inteligence generated content

AIGC）技术的兴起，遥感影像在新闻传播中眼见为实的权威性面临前所未有的挑战，因此旨在系统探讨遥感新闻权威性的社会建构过程、公众的感知模式及其在AIGC冲击下的潜在风险。首先，通过理论分析，解构遥感新闻权威性的三级建构机制，并据此提出关于公众感知的研究假设。其次，为验证这些假设，设计并实施一项包含412份有效样本的问卷调查。实证结果显示，公众总体上对遥感影像表现出较低的疑伪倾向，呈现出具有条件性的真实性预设；相关专业背景有助于改善遥感影像的辨识表现，但这种优势具有领域限定性，未能稳定延伸至非遥感影像。最后，分析AIGC技术造成的影像生成能力与鉴别、溯源及新闻核验能力之间的不对称，以及由此引发的虚假影像被采信与真实证据被否认的双重风险，并从来源认证、传播核验、媒介素养、法律约束与事实核查等方面提出相应的治理对策，为理解AIGC时代遥感新闻权威性的形成及其真实性风险提供了理论与实践参考。

Abstract

Objectives

Artificial inteligence generated content (AIGC) has sharply lowered the technical threshold for creating and editing remote sensing images

challenging the authority that such images have traditionally acquired from their apparent mechanical objectivity

institutional provenance

and evidentiary use in news narratives. The research addresses three connected questions: How the authority of remote sensing news is constructed

how audiences perceive the authenticity of remote sensing images

and how emerging authenticity risks can be governed.

Methods

Drawing on the sociology of scientific knowledge and visual communication

an analytical framework is developed at three levels: mechanical objectivity derived from technological recording

institutional trust transferred from scientific and journalistic organizations

and evidentiary meaning produced through visual framing. Audience perception is examined through an online questionnaire containing image-judgment tasks. A total of 412 valid responses were collected

and descriptive statistics and group comparisons were used to assess suspicion toward remote sensing and non-remote-sensing images

the detection of AI-generated images

false judgments of authentic images

and the cues used by respondents with different professional backgrounds.

Results

The results reveal a conditional authenticity presumption in favor of remote sensing images. In paired tasks where the objective authenticity of the images was held constant

the average proportion of respondents suspecting non-remote-sensing images was 56.1%

compared with 43.9% for remote sensing images. This relative trust advantage was not universal: prominent morphological anomalies

spatial conflicts

or other visual inconsistencies could override the effect of image type. Relevant educational or occupational experience improved performance mainly within the remote sensing domain. Respondents with partial or combined relevant backgrounds achieved higher detection rates for AI-generated remote sensing images than respondents without such backgrounds

while also producing fewer false judgments of authentic remote sensing images. Their attention shifted from surface features such as color

lighting

and unnatural edges toward physical plausibility

structural consistency

and the relationship between images and captions. However

the same advantage did not extend consistently to non-remote-sensing images

indicating that professional expertise provides domain-specific rather than general immunity to synthetic visual misinformation.

Conclusions

The central risk in the AIGC era therefore lies in the growing asymmetry between image-generation capabilities and the capacities for detection

provenance tracing

and journalistic verification. Synthetic images may exploit the professional appearance of remote sensing data to gain credibility

while authentic evidence may be rejected because any image is assumed to be generatable. Effective governance requires a coordinated framework that combines source authentication

verification throughout the communication process

platform labeling and accountability

public media literacy

legal constraints

and independent fact-checking. Such a framework can shift the basis of trust in remote sensing news from intuitive visual credibility to verifiable evidence-production processes.

关键词

Keywords

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