AIGC Image Features for GIS: A Preliminary Test of Elements, Colors, and Spatial Structure in Recommendation Tasks
文献类型：期刊论文
| 作者 | Wang, Qiang2,4; Yu, Zhihang3; Wang, Shu1,2,4; Zhu, Yunqiang1,2,4 |
| 刊名 | WEB AND WIRELESS GEOGRAPHICAL INFORMATION SYSTEMS, W2GIS 2025
 |
| 出版日期 | 2026 |
| 卷号 | 15946页码:20-30 |
| 关键词 | AIGC Modeling AIGC Image VGI Image Multimodal Recommendation Image Element Image Color Image Spatial structure |
| ISSN号 | 0302-9743 |
| DOI | 10.1007/978-3-032-01723-9_2 |
| 产权排序 | 1 |
| 文献子类 | Proceedings Paper |
| 英文摘要 | Volunteered Geographic Information (VGI) images are a vital source of visual information and image features in the GIS field. The advancement of artificial intelligence, particularly large language models and generative AI, now enables the generation of seemingly lifelike images from textual prompts (Artificial Intelligence Generated Content - AIGC). This raises a pertinent question: can AIGC image features serve as a viable alternative to VGI features in downstream GIS tasks, especially where VGI is scarce or difficult to obtain? This paper conducts an exploratory study comparing VGI and AIGC image features as inputs for a geographic recommendation model, specifically investigating the impact of image elements, colors, and spatial structures. The results indicate that current AIGC images, generated from general textual descriptions, cannot fully substitute for VGI images. This is primarily due to AIGC's challenges in accurately replicating the specific elements, colors, and spatial relationships inherent in real-world VGI. However, the study suggests AIGC images hold significant potential. When provided with more specific information about elements and particularly their colors, AIGC's performance approached, and in some color-focused tests, even slightly surpassed that of VGI images. This implies AIGC's main deficiency is its current understanding of real-world object characteristics and their visual representation, notably the basic knowledge of elements and their associated colors. We propose these shortcomings could be addressed by integrating geographic knowledge bases in future AIGC development. These findings aim to guide AIGC's application in GIS by identifying current limitations and areas for focused improvement. |
| URL标识 | 查看原文 |
| WOS研究方向 | Computer Science ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:001576340100002 |
| 出版者 | SPRINGER INTERNATIONAL PUBLISHING AG |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219737]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Wang, Shu |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 3.Nanjing Univ Posts & Telecommun, Coll Comp, Nanjing 210023, Peoples R China; 4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Qiang,Yu, Zhihang,Wang, Shu,et al. AIGC Image Features for GIS: A Preliminary Test of Elements, Colors, and Spatial Structure in Recommendation Tasks[J]. WEB AND WIRELESS GEOGRAPHICAL INFORMATION SYSTEMS, W2GIS 2025,2026,15946:20-30. |
| APA | Wang, Qiang,Yu, Zhihang,Wang, Shu,&Zhu, Yunqiang.(2026).AIGC Image Features for GIS: A Preliminary Test of Elements, Colors, and Spatial Structure in Recommendation Tasks.WEB AND WIRELESS GEOGRAPHICAL INFORMATION SYSTEMS, W2GIS 2025,15946,20-30. |
| MLA | Wang, Qiang,et al."AIGC Image Features for GIS: A Preliminary Test of Elements, Colors, and Spatial Structure in Recommendation Tasks".WEB AND WIRELESS GEOGRAPHICAL INFORMATION SYSTEMS, W2GIS 2025 15946(2026):20-30. |
入库方式： OAI收割
来源：地理科学与资源研究所
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