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.

The World Cultural Heritages are increasingly threatened with destruction by traditional causes of decay and socioeconomic development. The World Cultural Heritage in Hue, Central Vietnam is not an exception. In conservation management of the World Cultural Heritage satellite high-resolution images are among the sources providing detailed information on sites, monuments and/or their surroundings for assessment of the impacts of time and socioeconomic development on the site and monuments. The Complex of Monuments in Hue has been recognized by UNESCO in 1993 thanks to its specific features of Geomancy and architectural landscape. As such, these features are the targeted subject of local conservation management. This paper aims at testing six models of fusion CA, IHS, WT, GS, BT, UNB to find the most appropriate for information extraction from the optical image provided by the Vietnamese satellite VNREDSat-1 for monuments conservation management purpose. Two Monuments in Hue with two architectural concepts have been selected for this test, Hue Imperial City and the Tomb of the King Minh Mang. The results show that UNB model is appropriate for fusing 10 m multispectral data with 2.5 m panchromatic data of VNREDSat-1 for the extraction of water, vegetation and built objects and their spatial arrangement patterns inside the Monuments. The test on two architecturally representative monuments shows that object features extracted from the fused data are detailed enough to satisfy information requirement of conservation practice in Hue focusing mainly to protect the integrity and the authenticity of the monuments.

Extreme hydrological events become increasingly unpredictable due to climate change and sea-level rise, highlighting the importance of coastal sea level monitoring. This study aims to develop a Global Navigation Satellite System (GNSS) reflectometry technology that uses low-cost multi-frequency antennas to measure water levels. A multi-frequency GNSS antenna was installed in the Tam Giang lagoon area, Thua Thien Hue province, to collect data of GPS/GLONASS/Galileo/Beidou satellites at 1Hz from April 10 to April 29, 2022. Water level elevation is calculated from GNSS reflectometry data using Interference Pattern Technical (IPT) based on Signal-to-Noise Ratio (SNR). After filtering, the water level results are validated by data from the water level sensor located in the same location. The Root Mean Square Error between the water level from the GNSS - Reflectometry (GNSS– R) and the in situ measurement is 0,049 m and the correlation coefficient reaches 0,93 when combining different frequencies. The study results demonstrate that the multi-frequency GNSS-R station can be used as an additional method to measure water levels with an accuracy comparable to that of a standard tidal gauge. In addition, the study results also show the sensitivity of the GNSS reflected signal to weather conditions and the state of the sea surface, which is the basis for forecasting and early warning of storm surge extremes from GNSS reflectometry 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.