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Found 10 papers, 5 papers with code
Implicit Assimilation of Sparse In Situ Data for Dense & Global Storm Surge Forecasting
Hurricanes and coastal floods are among the most disastrous natural hazards.
Towards Large-scale Network Emulation on Analog Neuromorphic Hardware
We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system.
Monitoring water contaminants in coastal areas through ML algorithms leveraging atmospherically corrected Sentinel-2 data
For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A.
On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing
We anticipate that novel event-based vision datasets can be generated using this pipeline to support various spacecraft pose reconstruction problems given events as input, and we hope that the proposed methodology would attract the attention of researchers working at the intersection of neuromorphic vision and guidance navigation and control.
THRawS: A Novel Dataset for Thermal Hotspots Detection in Raw Sentinel-2 Data
Nevertheless, given the growing interest to apply Artificial Intelligence (AI) onboard satellites for time-critical applications, such as natural disaster response, providing raw satellite images could be useful to foster the research on energy-efficient pre-processing algorithms and AI models for onboard-satellite applications.
Decentralised Semi-supervised Onboard Learning for Scene Classification in Low-Earth Orbit
Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs.
Neuromorphic Computing and Sensing in Space
The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks.
Selected Trends in Artificial Intelligence for Space Applications
The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced.
Globally Optimal Event-Based Divergence Estimation for Ventral Landing
This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing.
MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels
The trained neural network achieves state-of-the-art results on EuroSAT with an accuracy that is up to 19. 76% better than previous methods depending on the number of labeled training examples.
Ranked #5 on
Scene Classification
on UC Merced Land Use Dataset
