| Abstract |
Object detection in remotely sensed (RS) satellite imagery has gained significance in smart city concepts, which include urban planning, disaster management, and environmental monitoring. Deep learning techniques have shown promising outcomes in object detection and scene classification from RS satellite images, surpassing traditional methods that are reliant on hand-crafted features. However, these techniques lack the ability to provide in-depth comprehension of RS images and enhanced interpretation for analyzing intricate urban objects with functional structures and environmental contexts. To address this limitation, this study proposes a framework that integrates a deep learning-based object detection algorithm with ontology models for effective knowledge representation and analysis. The framework can automatically and accurately detect objects and classify scenes in remotely sensed satellite images and also perform semantic description and analysis of the classified scenes. The framework combines a knowledge-guided ontology reasoning module into a YOLOv8 objects detection model. This study demonstrates that the proposed framework can detect objects in varying environmental contexts captured using a remote sensing satellite device and incorporate efficient knowledge representation and inferences with a less-complex ontology model. |
