  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.
