Cross Sensor

Cross-sensor research focuses on developing methods to effectively utilize data from multiple sensors with varying characteristics, aiming to improve the robustness and generalizability of machine learning models across diverse data sources. Current research emphasizes self-supervised learning techniques, often employing transformer architectures or other deep learning models, to align representations from different sensors, even with significant resolution or spectral differences. This work is crucial for advancing applications in remote sensing, autonomous driving, and smart manufacturing, where data fusion from heterogeneous sensors is essential for reliable and efficient system operation.