一个轻量级的农田绘图模型使用卫星图像
Maya Haj Hussain1, Diaa Addeen Abuhani1, Jowaria Khan1
1Department of Computer Science and Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种快速,经济高效的机器学习模型,用于使用卫星图像创建准确的农田地图. 该方法通过有效提供可靠的数据来增强全球作物生产和粮食安全评估.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 环境监测 环境监测
背景情况:
- 可靠的耕地地图对于农业,自然资源,环境,健康和可持续性至关重要.
- 手动绘制农田地图是劳动密集型,昂贵和耗时的,阻碍了全球粮食安全研究.
- 卫星图像为有效的耕地评估提供了一个潜在的解决方案.
研究的目的:
- 开发一种基于机器学习的,具有成本效益,快速和准确的作物土地测绘方法.
- 使用卫星数据创建可靠的模型来评估农田的面积和强度.
- 解决农业监测手动数据收集的局限性.
主要方法:
- 利用Sentinel-2卫星图像为四个不同的测试区域:伊朗,莫桑比克,斯里兰卡和苏丹.
- 实施了一个完整的管道,包括数据收集和时间序列重建.
- 应用机器学习模型用于耕地范围和作物强度映射,结合NDVI (标准化差异植被指数) 评分.
主要成果:
- 在所有四个测试区域的耕地绘制中实现了高准确度.
- 报告的准确度得分从0.92到0.98.9不等.
- 在不同的地理环境中证明了机器学习方法的有效性.
结论:
- 拟议的机器学习方法为耕地绘图提供了一种可靠和高效的方法.
- 这种技术为传统的手动绘图方法提供了具有成本效益的替代方案.
- 该模型的高精度支持改善全球农业监测和粮食安全评估.
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