TL;DR
A robust, interpretable, and physically consistent DL framework for reliable asteroid orbit classification in planetary defense applications is established using attention-driven Deep Learning and explainable techniques.
This work presents a standardized, open-source benchmark for evaluating state-of-the-art (SOTA) deep learning methods for Earth observation change detection, and reveals that well-optimized classical architectures, such as Siamese U-Nets, frequently outperform more complex contemporary models when computational efficie...
Accurate empirical tropospheric mapping functions (MFs) are essential for high-precision geodetic applications such as GNSS and VLBI. However, traditional MFs assume azimuthal symmetry or explicitly model gradients on regular grids, limiting their ability to represent real atmospheric conditions. To address the limitat...
Critical infrastructure location data is often incomplete and unevenly distributed globally, especially in developing regions. Earth observation foundation models are proposed as a new step in enabling us to more efficiently understand the natural and built environment, raising questions as to their effectiveness in pe...
Planetary surface exploration missions rely increasingly on autonomous robotic platforms capable of interpreting complex terrain to ensure safe navigation, enable targeted science, and improve operational efficiency, as demonstrated across past Mars missions from Viking through Perseverance. Among the key perception ca...
nnMNet is presented, a new baseline model designed for Martian terrain semantic segmentation that integrates linear attention to better capture global context and employ lightweight convolutions to reduce computational overhead and establish a new benchmark.
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