TransLand: An Adversarial Transfer Learning Approach for Migratable Urban Land Usage Classification using Remote Sensing

摘要

Urban land usage classification is a critical task in big data based smart city applications that aim to understand the social-economic land functions and physical land attributes in urban environments. This paper focuses on a migratable urban land usage classification problem using remote sensing data (i.e., satellite images). Our goal is to accurately classify the land usage of locations in a target city where the ground truth land usage data is not available by leveraging a classification model from a source city where such data is available. This problem is motivated by the limitation of current solutions that primarily rely on a rich set of ground-truth data for accurate model training, which encounters high annotation costs. Two important challenges exist in solving our problem: i) the target and source cities often have different urban characteristics that prevent the direct application of a model learned from the source city to the target city; ii) the complex visual features in satellite images make it non-trivial to “translate” the images from the target city to the source city for an accurate classification. To address the above challenges, we develop TransLand, an adversarial transfer learning framework to translate the satellite images from the target city to the source city for accurate land usage classification. We evaluate our scheme on the real-world satellite imagery and land usage datasets collected from live different cities in Europe. The results show that TransLand significantly outperforms the state-of-the-art land usage classification baselines in classifying the land usage of locations in a city.