48 citations · 63 across the 6 of their papers we have counts for
6 papers
Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks
Sagar Vaze, James Foley, Mohamed Seddiq +2
The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development. While Convolutional Neural Networks (CNNs) have made large gains in natural i…
SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework
Conrad James Foley, Sagar Vaze, Mohamed El Amine Seddiq +2
Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off. As climate change increases…
AI-based evaluation of the SDGs: The case of crop detection with earth observation data
Natalia Efremova, Dennis West, Dmitry Zausaev
The framework of the seventeen sustainable development goals is a challenge for developers and researchers applying artificial intelligence (AI). AI and earth observations (EO) can…
Prediction of Soil Moisture Content Based On Satellite Data and Sequence-to-Sequence Networks
Natalia Efremova, Dmitry Zausaev, Gleb Antipov
The main objective of this study is to combine remote sensing and machine learning to detect soil moisture content. Growing population and food consumption has led to the need to i…
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video
Boris Knyazev, Roman Shvetsov, Natalia Efremova +1
In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017. We propose an ensemble of several models, which capture spatial and audio featu…
Unsupervised Neural Architecture for Saliency Detection: Extended Version
Natalia Efremova, Sergey Tarasenko
We propose a novel neural network architecture for visual saliency detections, which utilizes neurophysiologically plausible mechanisms for extraction of salient regions. The model…