An active one-shot learning approach to recognizing land usage from class-wise sparse satellite imagery in smart urban sensing

摘要

Urban land usage recognition (ULUR) in smart urban sensing recognizes the physical attributes and socioeconomic functions of urban land resources using pervasive satellite imagery data. Reliable ULUR results are essential for urban management and planning applications. A key limitation of current ULUR solutions is that they cannot identify land usage classes that are not included in the training data (i.e., untrained classes). To address this limitation, this study investigates a new class-wise sparse ULUR problem in which the goal is to learn an accurate ULUR model that can effectively identify the land usage classes of images of the untrained classes. Two critical challenges exist in resolving this problem: (i) How can the ULUR model learn the unknown class-specific visual characteristics for untrained classes without ground truth labels? (ii) How can we effectively generalize the ULUR model from trained classes to untrained ones given the excessive and fine-grained object details in the images? This paper proposes SparseLand, an active one-shot learning approach, to address these challenges using a novel duo-branch deep recognition framework. The results of a real-world ULUR application demonstrated that SparseLand clearly outperforms state-of-the-art baselines with the highest ULUR accuracy under different application scenarios.