Mapping Approach

Mapping approaches encompass diverse techniques for establishing correspondences between different data representations, aiming to translate information across domains or improve data efficiency. Current research focuses on leveraging machine learning, particularly deep neural networks (including variational autoencoders and transformers) and graph-based methods, to automate and improve the accuracy of mapping processes across various applications. These advancements are significant for diverse fields, including robotics (e.g., efficient navigation and object recognition), computer vision (e.g., image processing and semantic segmentation), and data management (e.g., ontology matching and map updating), enabling more efficient and robust systems. The development of novel mapping strategies is crucial for addressing challenges in data heterogeneity and computational complexity.