使用机器学习算法进行土地利用和土地覆盖 (LULC) 性能建模:澳大利亚墨尔本市的案例研究
Jagannath Aryal1, Chiranjibi Sitaula2, Alejandro C Frery3
1Earth Observation and AI Research Group, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, 3053, Australia. jagannath.aryal@unimelb.edu.au.
Scientific reports
|August 19, 2023
概括
这项研究自动化了土地利用和土地覆盖 (LULC) 性能建模,使用机器学习算法进行城市规划. 随机森林在分类来自Sentinel-2A图像的LULC地图方面表现出卓越的准确性和稳定性.
科学领域:
- 地理空间分析是什么?
- 环境科学环境科学
- 城市规划是城市规划.
背景情况:
- 准确的土地使用和土地覆盖 (LULC) 数据对于有效的城市规划至关重要.
- 从卫星图像中对LULC类别进行分类是一项具有挑战性但至关重要的任务.
- 之前的LULC绘图工作已经探索了光谱,统计和基于索引的特征.
研究的目的:
- 通过使用机器学习 (ML) 算法,自动化LULC性能建模以实现稳定性.
- 评估七个自动化方案的有效性,其中有六个ML算法用于LULC分类.
- 为城市规划应用提供LULC地图的强大测量.
主要方法:
- 为LULC性能建模设计了七个自动化方案.
- 应用了六个ML算法:随机森林,SVM (线性和RBF内核),ANN,天真贝叶斯和GLM.
- 利用了澳大利亚墨尔本市的Sentinel-2A卫星图像.
- 对不同训练数据进行了稳定性和统计分析.
主要成果:
- 随机森林在所有方案中实现了最高的分类准确性 (0.99).
- 随机森林表现出高强度,在所有五个类别中保持F1得分超过0.99.
- 统计分析 (Wilcoxon排名测试) 证实了ML算法的稳定性 (p值<0.05).
结论:
- 拟议的自动化方案有效地模拟了LULC分类性能.
- 随机森林是一个非常准确和强大的ML算法,用于城市地区的LULC映射.
- 这项工作提供了一种可靠的方法,用于生成对城市规划至关重要的强大的LULC地图.
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