用遥感与机器学习分析斐济纳迪地区11年土地利用变化。
Landcover classification and change detection using remote sensing and machine learning: a case study of Western Fiji
- 结合遥感影像与监督学习构建分类模型
- 2013–2024年城市扩张显著，识别出明显变化区域
- 适合关注城市化监测的地理与环境研究者
作为发展中国家，斐济正经历快速城市化，表现为大规模住房、道路和公共工程建设项目。本研究以斐济纳迪地区为例，利用2013至2024年的遥感数据，构建机器学习与遥感框架，实现土地利用/土地覆盖变化检测。研究采用Landsat-8卫星影像，通过人工标注建立监督学习训练集，结合Google Earth Engine与无监督k-means聚类生成地表覆盖图，并应用卷积神经网络对选定区域进行地表类型分类。最终实现变化检测结果的可视化，突出显示城市区域随时间的演变过程，为土地覆盖/利用建模与动态监测提供技术支撑。
原文摘要 · Abstract (English)
As a developing country, Fiji is facing rapid urbanisation, which is visible in the massive development projects that include housing, roads, and civil works. In this study, we present machine learning and remote sensing frameworks to compare land use and land cover change from 2013 to 2024 in Nadi, Fiji. The ultimate goal of this study is to provide technical support in land cover/land use modelling and change detection. We used Landsat-8 satellite image for the study region and created our training dataset with labels for supervised machine learning. We used Google Earth Engine and unsupervised machine learning via k-means clustering to generate the land cover map. We used convolutional neural networks to classify the selected regions' land cover types. We present a visualisation of change detection, highlighting urban area changes over time to monitor changes in the map.
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