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Drones are capable of exploring large areas due to their swift mobility and utilization efficiency, so their use in various fields such as surveillance is increasing. When a drone performs its mission, the estimation of drone's location relies on GPS signals. Therefore, it becomes difficult to accurately estimate the drone's location in environments where GPS signals are unstable or in denied areas, which directly affects the safety and mission performance of the drone. Drone missions by autonomous flight are often carried out along a given route. This can be done by extracting and comparing the feature points of the image data from the camera mounted on the drone and the image data on the already constructed map using the SIFT algorithm. It allows the drone’s location to be estimated under GPS-denied environments. In this paper, we aim to construct aerial map data and verify the technology for estimating and correcting the drone’s location through image data received from the drone. [ABSTRACT FROM AUTHOR]

Multimodal refers to the combination or integration of data from multiple sources or modalities. Multimodal data integration involves merging diverse sources, such as imaging, clinical, and genetic data, to obtain a more comprehensive view of patient health. This process, facilitated by deep learning, has emerged as a transformative technique in the biomedical field, enabling a comprehensive understanding of patient health and leading to improved decision-making. This study provides an overview of the general pipeline for multimodal data integration techniques. We start by examining different types of biomedical data and exploring feature extraction and preprocessing techniques for these modalities. Moreover, we discuss data-level, feature-level, and decision-level fusion methods in detail, specifying scenarios where each fusion approach is most effective. The review also addresses the use cases and existing applications of multimodal data fusion, highlighting their impact on disease diagnosis, prognosis, treatment planning, and personalized medicine. [ABSTRACT FROM AUTHOR]