2 code implementations • 17 Jul 2023 • Vít Růžička, Gonzalo Mateo-García, Chris Bridges, Chris Brunskill, Cormac Purcell, Nicolas Longépé, Andrew Markham
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.
1 code implementation • 26 Nov 2022 • Beichen Zhang, Huiqi Wang, Amani Alabri, Karol Bot, Cole McCall, Dale Hamilton, Vít Růžička
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.
1 code implementation • 23 Sep 2022 • Amine M'Charrak, Vít Růžička, Sangyun Shin, Madhu Vankadari
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.
1 code implementation • 4 Nov 2021 • Vít Růžička, Anna Vaughan, Daniele De Martini, James Fulton, Valentina Salvatelli, Chris Bridges, Gonzalo Mateo-Garcia, Valentina Zantedeschi
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.
1 code implementation • 25 Aug 2020 • Vít Růžička, Stefano D'Aronco, Jan Dirk Wegner, Konrad Schindler
We investigate active learning in the context of deep neural network models for change detection and map updating.
1 code implementation • 5 Aug 2019 • Vít Růžička, Eunsu Kang, David Gordon, Ankita Patel, Jacqui Fashimpaur, Manzil Zaheer
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.
1 code implementation • 24 Oct 2018 • Vít Růžička, Franz Franchetti
Machine learning has celebrated a lot of achievements on computer vision tasks such as object detection, but the traditionally used models work with relatively low resolution images.