Cloud and cloud shadow cause information loss in optical remote sensing analysis. South East Asia, especially Vietnam, Sentinel-2 imagery has short re-visit cycle and observations tend to be contaminated with cloud and cloud shadow. Traditional cloud removal methods require close date multi-temporal data to avoid seasonal land cover changes. In this study, a method of integrating Deep Convolutional Neural Networks (DCNN) and Generative Adversarial Network (GAN) was proposed. This machine learning model estimates the information loss over cloud contaminated areas on a single Sentinel-2 image. The results show that for images with cloud cover rate under 25%, our model can reconstruct cloudless images with PSNR (25 – 40 dB) and SSIM (0.86 – 0.93) compared to real clear images. On the other hand, with cloud cover rate up to 40%, the model performance will be affected heavily by the distribution of cloud and cloud shadow areas. By investigating DCNN and GAN, our method has proven to be an effective tool to remove cloudy images with low and medium rates, which enriches the clear optical remote sensing data sources for environment monitoring.

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

Tourism is one of the smokeless industries that has been developing rapidly, opening up many job opportunities as well as socio-economic development for many countries around the world. In Vietnam, the role of the tourism industry in the development of the country has been well recognized and has received early investment attention from the Party and the State. Quang Binh is a central province of Vietnam blessed with many natural beauty, historical sites, which is also a place attracting many tourists from all over the world. However, the management of tourist site information as well as the promotion of tourism support of Quang Binh province is still inadequate. Tourists still have to rely on maps, guidebooks, through word of mouth or experience or travel companies to determine travel schedules leading to failure to meet their own requirements. Base on powerful of the Internet and digital mapping technology, the authors have conducted research to build a Web site that supports automated travel schedules to assist domestic and foreign tourists, support development and increase competitiveness for tourism in Quang Binh province.