EO-Based Land Cover and Land Use Classification in Vietnam
Earth Observation and AI-based super-resolution techniques were used in Hanoi, Vietnam to improve urban land cover mapping, supporting air pollution research, public health studies, and data-driven urban planning.
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Description
Earth Observation (EO) was used in Hanoi, Vietnam to map urban land use and support research on air pollution and public health. The work, carried out by the Asian Development Bank (ADB) and supported by the GDA Fast EO Co-Financing Facility (FFF), combined satellite imagery with AI-based methods to better identify green spaces and other urban features.
The project employed high-resolution satellite images and deep learning algorithms to classify green spaces and urban characteristics with high accuracy, while also investigating super-resolution methods to enhance freely available Sentinel-2 data. By improving the spatial detail of satellite imagery, it produced more precise and scalable land use maps that support analysis of how people respond to air quality information and move through urban environments.
The project establishes a strong foundation for broader integration of EO into air pollution management and public health analysis in Hanoi. It also shows how EO may be integrated into ADB procedures to assist development finance goals when paired with super-resolution techniques.
Read more about this Case Study here: Enhancing Earth Observation for land cover and land use classification in Vietnam