In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application.

翻译：在气候变化日益加剧的背景下，土地覆盖制图已成为环境监测中的一项迫切需求。土地覆盖分类的准确性越来越依赖于遥感数据的改进。近年来，利用卫星影像进行土地覆盖分类的研究日益普遍，但现有方法仍存在主观性强、耗时较长等缺陷。为克服这些局限，已有研究采用多种深度学习技术。然而，多数研究仅使用单一影像类型来评估土地覆盖制图算法。为此，本研究在多光谱、高光谱及高空间分辨率航空影像数据集上开展深度学习语义分割实验，以实现土地覆盖制图。研究采用Unet、Linknet、FPN和PSPnet等语义分割方法对植被、水体及其他类别（如土壤与不透水表面）进行分类。LinkNet模型在所有数据集中均获得0.92的较高交并比（IoU）精度，其性能与其他提及的技术相当。在不同影像类型的评估中，多光谱影像表现出更优性能，其IoU与F1分数分别达到0.993和0.997。本研究结果凸显了LinkNet模型与多光谱影像在土地覆盖分类中的高效性与广泛适用性。此项研究通过开源方式构建土地覆盖分割方法，为未来长期应用提供了技术基础。