用大模型让遥感图像自动分析城市变化，支持复杂问题推理
LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery
- 构建分层代理框架，融合大模型与视觉模型，减少幻觉
- 在140个真实场景问题上达到90.71%查询准确率，尤其擅长多步推理
- 适合需要智能解析遥感图像变化的规划、环保与城市管理人群
现有变化检测方法往往难以应对多样化的现实问题，且缺乏深度分析能力。本文提出通用代理框架ChangeGPT，整合大型语言模型（LLM）与视觉基础模型，采用分层结构以缓解幻觉问题。在包含140个问题的定制数据集上评估，问题按真实场景分类，涵盖尺寸、类别、数量等不同类型与复杂度。评估指标包括工具选择的精确率/召回率及整体查询准确率（Match）。ChangeGPT在使用GPT-4-turbo后端时表现最优，达到90.71%的匹配率，尤其在需多步推理和稳健工具选择的变化类查询中优势显著。通过深圳前海湾真实城市变化监测案例验证了其实际有效性。ChangeGPT通过提供智能化、可适应性及多类型变化分析能力，为遥感应用中的决策支持提供了有力解决方案。
原文摘要 · Abstract (English)
Existing change detection methods often lack the versatility to handle diverse real-world queries and the intelligence for comprehensive analysis. This paper presents a general agent framework, integrating Large Language Models (LLM) with vision foundation models to form ChangeGPT. A hierarchical structure is employed to mitigate hallucination. The agent was evaluated on a curated dataset of 140 questions categorized by real-world scenarios, encompassing various question types (e.g., Size, Class, Number) and complexities. The evaluation assessed the agent's tool selection ability (Precision/Recall) and overall query accuracy (Match). ChangeGPT, especially with a GPT-4-turbo backend, demonstrated superior performance, achieving a 90.71 % Match rate. Its strength lies particularly in handling change-related queries requiring multi-step reasoning and robust tool selection. Practical effectiveness was further validated through a real-world urban change monitoring case study in Qianhai Bay, Shenzhen. By providing intelligence, adaptability, and multi-type change analysis, ChangeGPT offers a powerful solution for decision-making in remote sensing applications.
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