Deep learning applied to urban agriculture: spatial-temporal changes of agricultural land in a rapidly urbanizing Southeast Asian city

Rapid urbanization in Southeast Asia has been posing huge impacts on local food systems, altering spatial-temporal patterns of urban agriculture, ecosystems and social life. Understanding these changes is crucial for cities planning their land use and infrastructure development to achieve a balance between urban growth, agricultural sustainability, and food security. This study mapped the changes between 2013 and 2020 of five agricultural types within (peri)urban areas in Huế, a province’s capital in Vietnam. High-resolution SPOT satellite images (1.5m) and a deep learning model based on the U-net architecture were used to map land use and agriculture types. This approach addresses challenges in generating extensive labelled datasets in urban settings characterized by fragmented farmland and dense development. The optimized U-net model achieved high classification performance (for 2013: IoU = 0.86 and Kappa = 0.93, for 2020: IoU = 0.87 and Kappa = 0.92) even when operated on regular CPU computers, demonstrating its practical applicability for countries with limited technical infrastructure. This is also the first study in Southeast Asia to accurately map (overall accuracy 85% for 2013 and 87% for 2020) multiple types of urban agriculture at 1.5 m resolution, enabling detailed spatial-temporal changes analysis. These results can inform decision-makers in elaborating effective land use strategies and food security plans, and offer researchers a scalable deep learning framework for urban agriculture mapping in rapidly urbanizing regions.