基于变压器的土地利用和土地覆盖的分类,可以使用卫星图像进行解释
Mehak Khan1, Abdul Hanan2, Meruyert Kenzhebay2
1Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway. mehakkhan3@hotmail.com.
Scientific reports
|July 20, 2024
概括
这项研究通过平衡计算成本和准确性来优化土地利用和土地覆盖 (LULC) 分析的变压器模型. 转移学习和微调提高了LULC应用程序的效率和透明度.
科学领域:
- 遥感 遥感 遥感 遥感
- 人工智能的人工智能
- 地理空间分析的研究.
背景情况:
- 变压器模型具有先进的土地利用和土地覆盖 (LULC) 分析,但遭受高计算成本.
- 这些强大的模型的实际实施受到资源需求的阻碍.
研究的目的:
- 为了平衡基于变压器的LULC分析中的计算成本和准确性.
- 提高对人工智能驱动的LULC模型的透明度和信任.
- 探索可解释AI (XAI) 在这个领域的应用.
主要方法:
- 利用转移学习和微调策略来优化变压器模型.
- 雇佣Captum用于偏见检测和AI决策的解释,确保透明度.
- 应用于卫星图像的方法用于LULC分类.
主要成果:
- 转移学习显示了改进基于变压器的卫星图像分类的潜力.
- 战略微调保持了模型效率,对准确度的影响最小.
- 卡普图姆促进了潜在偏差的发现和缓解.
结论:
- 基于变压器的模型,优化转移学习和微调,提供高效和准确的LULC分析.
- 可解释性AI (XAI) 对于透明和可信的LULC建模至关重要.
- 这种方法促进了公平性和在城市规划和环境研究等领域的更广泛采用.
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