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优化深度学习模型以从无人机图像和遥感数据中检测气候相关的自然灾害

Kim VanExel¹, Samendra Sherchan², Siyan Liu³

¹Bioenvironmental Sciences Department, Morgan State University, Baltimore, MD 21251, USA.

Journal of imaging

|February 25, 2025

概括

人工智能 (AI) 模型通过空中图像准确地检测到洪水和荒漠化等自然灾害. 这种人工智能方法为环境监测和气候变化适应提供了一个新的解决方案.

科学领域:

环境科学环境科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

气候变化正在增加自然灾害的频率和强度.
有效监测和早期发现这些灾害对于减轻和应对至关重要.
空中影像为灾难评估提供了有价值的数据,但自动化分析具有挑战性.

研究的目的:

开发和评估人工智能 (AI) 模型,以从空中图像中检测自然灾害,特别是洪水和荒漠化.
创建和利用一个新的数据集,气候变化数据集,用于训练和比较深度学习模型.
展示AI在应对环境挑战和支持气候变化适应方面的潜力.

主要方法:

一个新的数据集,气候变化数据集,由来自无人机和卫星的6334张空中图像编制.
训练了四个机器学习 (ML) 模型,包括卷积神经网络 (CNN),DenseNet201,VGG16和ResNet50.
模型性能进行了比较,并选择了DenseNet201进行优化. 进行了交叉验证.

主要成果:

所有四种ML模型都在检测自然灾害方面表现出很高的性能.
丹斯网201和ResNet50的测试准确度最高,分别为99.37%和99.21%.
这些模型成功地将图像分为三个类别:洪水,沙漠和没有.

结论:

人工智能,特别是深度学习模型,显示出从空中图像中准确高效地检测自然灾害的巨大潜力.
开发的气候变化数据集和优化的ML模型为环境监测和研究提供了宝贵的资源.
这种由人工智能驱动的方法可以增强灾害应对能力,有助于环境可持续性,并有助于气候变化适应战略.

关键词:

在这里,我们可以看到AIAIAI.在美国,CNN是CNN.无人机无人驾驶飞行器 (UAV) 是一种无人机.气候变化气候变化气候变化荒漠化,荒漠化是什么洪水洪水洪水洪水神经网络的神经网络的神经网络远程传感是一种遥感技术.卫星卫星卫星卫星卫星卫星卫星卫星卫星.转移学习转移学习

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