JAXA’s new high-resolution land use land cover map for Vietnam using a time-feature convolutional neural network

• Published in: Scientific reports 2024-02, Vol.14 (1), p.3926-3926, Article 3926
• Main Authors: Truong, Van Thinh, Hirayama, Sota, Phan, Duong Cao, Hoang, Thanh Tung, Tadono, Takeo, Nasahara, Kenlo Nishida
• Format: Article
• Language: English
• Subjects: 704/172/4081; 704/844/685; Accuracy; Biodiversity; Climate models; Disaster management; Emergency preparedness; Humanities and Social Sciences; Land use; multidisciplinary; Natural resources; Neural networks; Resource conservation; Science; Science (multidisciplinary)
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-54308-1

Land use land cover (LULC) maps are crucial for various applications, such as disaster management, natural resource conservation, biodiversity evaluation, climate modeling, etc. The Japan Aerospace Exploration Agency (JAXA) has released several high-resolution LULC maps for national and regional scales. Vietnam, due to its rich biodiversity and cultural diversity, is a target country for the production of high-resolution LULC maps. This study introduces a high-resolution and high-accuracy LULC map for Vietnam, utilizing a CNN approach that performs convolution over a time-feature domain instead of the typical geospatial domain employed by conventional CNNs. By using multi-temporal data spanning 6 seasons, the produced LULC map achieved a high overall accuracy of 90.5% ± 1.2%, surpassing other 10-meter LULC maps for Vietnam in terms of accuracy and/or the ability to capture detailed features. In addition, a straightforward and practical approach was proposed for generating cloud-free multi-temporal Sentinel-2 images, particularly suitable for cloudy regions. This study marks the first implementation of the time-feature CNN approach for the creation of a high-accuracy LULC map in a tropical cloudy country.