在基于深度学习的地图概括中推理地图知识,使用可解释的AI
Cheng Fu1, Zhiyong Zhou1, Yanan Xin2
1Department of Geography, University of Zurich, Zurich, Switzerland.
概括
将可解释的人工智能 (XAI) 集成到深度神经网络 (DNN) 中,用于自动地图概括,揭示了学习的地图知识. 这种方法通过关注建筑边界等关键特征来增强DNN开发.
科学领域:
- 地理信息科学 地理信息科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 尽管经过数十年的研究,自动化地图图谱概括仍然具有挑战性.
- 深度神经网络 (DNN) 显示出部分自动化的前景,但通常被视为黑子.
- 在DNN中缺乏透明度,阻碍了学习的地图原理的理解和改进.
研究的目的:
- 将可解释的AI (XAI) 集成到基于深度学习 (DL) 的地图概括中.
- 了解DNNs.NN.所学到的地图知识.
- 通过透明度开发和完善DL模型,以实现地图通用化.
主要方法:
- 一个使用XAI框架的经验案例研究.
- 将视觉分析和定量实验应用于预训练的ResU-Net模型.
- 输入特征对地图概括预测的重要性的分析.
主要成果:
- 基于XAI的可视化可以被人类专家解释.
- 在DNN中,建筑界限优先于内部建筑特征.
- 边界交叉与联合被提议作为基于的概括的高级评估指标.
结论:
- 对于增强基于DL的地图概括,XAI是必要的,也是可行的.
- 了解学习特征可以改善自动化概括系统的开发.
- 该研究倡导将XAI集成到未来的地图通用化框架中.
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