Delivering High-Resolution Historical Remote Sensing Image using Integration of SRGAN

High-resolution remote sensing images are used in Earth mapping and sensing. With high resolution, we can see clearer details and get more accurate information about topography, vegetation, land use, and environmental changes such as deforestation, climate change, and natural disasters. This data is important for environmental monitoring, urban planning, natural resource management, disaster mitigation, and integrated real time monitoring. However, the data resource for Indonesia are still limited to time and space, especialy for historical data. The importance of high-resolution remote sense imagery lies in its ability to provide detailed and accurate data in various fields in which can be improved by the acceleration of artificial intelligent development. SRGAN is one of the algorithm that promising in way to improve the quality of images. Here we present the improvement of Landsat Image from 2000 to 2020 for P126R62 with the integration of SRGAN algorithm to perform four times improvement of pixels availibility. The data showed that the algorithm worked properly to improve and reduced the cloud with the loss rate of 0.000156 in discriminator loss and 0.549 in generator loss showing the model fitted to be applied over remote sensing images.