Detection of Forest Fires through Deep Unsupervised Learning Modeling of Sentinel-1 Time Series
With an increase in the amount of natural disasters, the combined use of cloud-penetrating Synthetic Aperture Radar and deep learning …
Grad-SLAM: Explaining Convolutional Autoencoders’ Latent Space of Satellite Image Time Series
This paper introduces a tool for explaining the latent space generated by applying convolutional autoencoders to satellite image time …
FARMSAR: Fixing AgRicultural Mislabels Using Sentinel-1 Time Series and AutoencodeRs
This paper aims to quantify the errors in the provided agricultural crop types, estimate the possible error rate in the available …
Modelling of agricultural SAR Time Series using Convolutional Autoencoder for the extraction of harvesting practices of rice fields
We apply an unsupervised learning methodology to project SAR Time Series of growing rice fields onto a 3-dimensional space, where we …
Extracting relevance from SAR temporal profiles on a glacier and an alpine watershed by a deep autoencoder
This paper proposes to use methods for compressing the temporal profiles of Sentinel-1 images, in order to be able to evaluate and …
Beets or Cotton? Blind Extraction of Fine Agricultural Classes Using a Convolutional Autoencoder Applied to Temporal SAR Signatures
We present a fully unsupervised learning pipeline, which involves both a projection method and a clustering algorithm dedicated to the …
Multi-branch Deep Learning model for detection of settlements without electricity
We introduce a multi-branch Deep Learning architecture that allows for the extraction of multi-scale features. Exploiting the data …
Convolutional Autoencoder for unsupervised representation learning of PolSAR Time-Series
Temporal Convolutional AutoEncoders are used as feature extractors to project time series onto a latent space where similarity …
MSc Thesis: Multimodal Similarity Learning for Duplicate Product Identification
State of the art models for Similarity Learning are all based on Deep Learning architecture using Siamese Network [Gregory et al., …
