Scope and Topics of interest include (but are not limited to):

1. Object Detection and Recognition:
   • Detection of objects, vehicles, buildings, and infrastructure in aerial and street-level images
   • Recognition of complex structures and landmarks from different perspectives.
2. Semantic Segmentation and Scene Understanding:
   • Semantic segmentation of urban environments, roads, and natural landscapes.
   • Scene understanding through the identification and classification of various elements.
3. Change Detection and Monitoring:
   • Detection of changes over time in aerial and street-level imagery.
   • Monitoring of urban development, environmental changes, and infrastructure modifications.
4. Deep Learning Approaches:
   • Application of deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), for element detection.
   • Transfer learning and domain adaptation for enhancing performance across different imaging modalities.
5. Multi-modal Fusion and Integration:
   • Fusion of data from diverse sources such as satellite imagery, drone footage, and street-level images.
   • Integration of multi-modal data for comprehensive scene understanding and analysis.
6. Real-time and Efficient Algorithms:
   • Development of real-time algorithms for efficient processing of large-scale aerial and street-level imagery.
   • Optimization techniques for improving computational efficiency without compromising accuracy.
7. Applications and Use Cases:
   • Applications in urban planning, disaster management, environmental monitoring, and surveillance.
   • Use cases demonstrating the practical significance of element detection in diverse domains.