The recent advancements in the field of remote sensing and disaster response have seen a significant shift towards leveraging multimodal data and innovative neural network architectures to enhance detection and classification accuracy. Researchers are increasingly focusing on developing lightweight, efficient models that can operate in real-time, particularly in resource-constrained environments such as those encountered during disaster scenarios. The integration of graph-based neural networks, attention mechanisms, and transformer architectures is proving to be particularly effective in capturing complex relationships and global contexts within the data. Additionally, the use of UAV imagery and the development of specialized datasets are playing crucial roles in advancing the field, enabling more accurate and timely responses to environmental changes and emergencies. Notably, the introduction of hybrid models that combine the strengths of convolutional neural networks and vision transformers is showing promising results in both accuracy and computational efficiency, making them ideal for deployment in UAV-based systems. These developments collectively indicate a move towards more sophisticated, context-aware, and real-time capable systems in remote sensing and disaster management.