Ruoling Wu
ABSTRACT
Spatial scene classification has long been a prominent area of research in the field of geographic information science. In the past, traditional approaches heavily relied on retrieval methods based on image features. However, given the rapid advancements in deep learning and artificial intelligence, the efficient classification of complex spatial scenes has become increasingly crucial. This paper presents a novel framework named WYA (Where You At) that combines surrounding object detection with knowledge graph to automate the process of spatial scene classification. Initially, the input images undergo processing using object detection techniques to identify key entities within the scenes. Subsequently, a knowledge graph, which encompasses various spatial scenes, entities, and their relationships, is utilized to identity spatial scene catogories. To validate the effectiveness of the framework, experiments were conducted using eight spatial scene categories as an example. The results demonstrated a high level of consistency with actual spatial types, thus affirming the efficacy of the framework and highlighting its potential application value in the domain of spatial scene classification.
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Index Terms
- WYA: A Novel Spatial Scene Classification Framework Based on Surrounding Object Detection
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