Flood monitoring systems are crucial for flood management and consequence mitigation in flood prone regions. Different remote sensing techniques are increasingly used for this purpose. However, the different approaches suffer various limitations, including cloud and weather effects (optical data), and low spatial resolution and poor colour presentation (synthetic aperture radar data). This study fuses two data types (Landsat and Sentinel-1) to overcome these limitations and produce better quality images for a prototype flood application in the Vietnam Open Data Cube (VODC). Visual and quantitative evaluation of fused image quality revealed improvement in the images compared with the original scenes. Ground-truth data was used to develop the study flood extraction algorithm and we found a good agreement between our results and SERVIR Mekong (a joint initiative by the US agency for International Development (USAID), National Aeronautics and Space Administration (NASA), Myanmar, Thailand, Cambodia, Laos and Vietnam) maps. While the algorithm is run on a personal computer (PC), it has a clear potential to be developed for application on a big data system.
Abstract. There are two main topics in this paper: (i) Vietnamese words are recognized and sentences are segmented into words by using probabilistic models; (ii) the optimum probabilistic model is constructed by an unsupervised learning processing. For each probabilistic model, new words are recognized and their syllables are linked together. The syllable-linking process improves the accuracy of statistical functions which improves contrarily the new words recognition. Hence, the probabilistic model will converge to the optimum one. Our experimented corpus is generated from about 250.000 online news articles, which consist of about 19.000.000 sentences. The accuracy of the segmented algorithm is over 90%. Our Vietnamese word and phrase dictionary contains more than 150.000 elements.
Currently, 55% of the world’s population lives in urban areas, this proportion is expected to increase to 68% by 2050. In the next 30 years, a large amount of the world's population is predictably concentrated in urban areas in the developing world. Ha Noi is the capital and largest city of Viet Nam which has the average growth rate of approximately 3% per year. Urban developmentmanagement has become an important issue in Viet Nam since the negative impacts of the urban sprawl on the environmental sustainability, life quality has been increasing as well. Hence, urban planning and management would be pivotal for creating the effective framework conditions for a sustainable development. The objective of our study is to explore the urban growth of Ha Noi using the Landsat images from 1975 to 2020 compared to the city planning. The volatility analysis information from classified urbanland maps is considered supportive for urban management and planning oriented work. In addition, the remote sensing data analysis is a useful tool to support planners, managers for urban management and decision. This study results showed the urban land area in study site city has been growing about 3 times and the largest rate (4-6 times) for the Dong Anh, Tu Liem, Gia Lam and smallest rate (10-25%) for Ba Dinh and Hai Ba Trung districts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.