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Found 7 papers, 7 papers with code
Fast model inference and training on-board of Satellites
In this work we demonstrate the reliable use of RaVAEn onboard a satellite, achieving an encoding time of 0. 110s for tiles of a 4. 8x4. 8 km$^2$ area.
Unsupervised Wildfire Change Detection based on Contrastive Learning
The aim of this study is to develop an autonomous system built on top of high-resolution multispectral satellite imagery, with an advanced deep learning method for detecting burned area change.
Tighter Variational Bounds are Not Necessarily Better. A Research Report on Implementation, Ablation Study, and Extensions
We provide theoretical and empirical evidence that increasing the number of importance samples $K$ in the importance weighted autoencoder (IWAE) (Burda et al., 2016) degrades the signal-to-noise ratio (SNR) of the gradient estimator in the inference network and thereby affecting the full learning process.
Unsupervised Change Detection of Extreme Events Using ML On-Board
In this paper, we introduce RaVAEn, a lightweight, unsupervised approach for change detection in satellite data based on Variational Auto-Encoders (VAEs) with the specific purpose of on-board deployment.
Deep Active Learning in Remote Sensing for data efficient Change Detection
We investigate active learning in the context of deep neural network models for change detection and map updating.
The Myths of Our Time: Fake News
While the purpose of most fake news is misinformation and political propaganda, our team sees it as a new type of myth that is created by people in the age of internet identities and artificial intelligence.
