Deep Space
Deep space research encompasses a broad range of scientific inquiries, primarily focused on understanding and utilizing vast datasets from various sources, including satellite imagery, sensor readings, and astronaut photography. Current research heavily employs machine learning, particularly convolutional neural networks and transformers, to analyze these datasets for tasks like object detection, classification, and regression, often within specialized embedding spaces to improve efficiency and accuracy. These advancements are crucial for improving Earth observation, enabling more effective climate change mitigation (e.g., methane detection), optimizing resource management (e.g., aquaculture monitoring), and enhancing the efficiency and safety of space exploration.
Papers
RepMatch: Quantifying Cross-Instance Similarities in Representation Space
Mohammad Reza Modarres, Sina Abbasi, Mohammad Taher Pilehvar
Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks
Sabrina Herbst, Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic
Machine Learning for Methane Detection and Quantification from Space - A survey
Enno Tiemann, Shanyu Zhou, Alexander Kläser, Konrad Heidler, Rochelle Schneider, Xiao Xiang Zhu
Self-supervised Topic Taxonomy Discovery in the Box Embedding Space
Yuyin Lu, Hegang Chen, Pengbo Mao, Yanghui Rao, Haoran Xie, Fu Lee Wang, Qing Li