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Found 8 papers, 0 papers with code
Soil Organic Carbon Estimation from Climate-related Features with Graph Neural Network
Soil organic carbon (SOC) plays a pivotal role in the global carbon cycle, impacting climate dynamics and necessitating accurate estimation for sustainable land and agricultural management.
AI for Agriculture: the Comparison of Semantic Segmentation Methods for Crop Mapping with Sentinel-2 Imagery
Crop mapping is one of the most common tasks in artificial intelligence for agriculture due to higher food demands from a growing population and increased awareness of climate change.
Optimal Use of Multi-spectral Satellite Data with Convolutional Neural Networks
The analysis of satellite imagery will prove a crucial tool in the pursuit of sustainable development.
SMArtCast: Predicting soil moisture interpolations into the future using Earth observation data in a deep learning framework
Soil moisture is critical component of crop health and monitoring it can enable further actions for increasing yield or preventing catastrophic die off.
AI-based evaluation of the SDGs: The case of crop detection with earth observation data
The framework of the seventeen sustainable development goals is a challenge for developers and researchers applying artificial intelligence (AI).
Prediction of Soil Moisture Content Based On Satellite Data and Sequence-to-Sequence Networks
We achieve this by applying satellite imagery, crop segmentation, soil classification and NDVI and soil moisture prediction on satellite data, ground truth and climate data records.
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video
In this paper we describe a solution to our entry for the emotion recognition challenge EmotiW 2017.
Unsupervised Neural Architecture for Saliency Detection: Extended Version
We propose a novel neural network architecture for visual saliency detections, which utilizes neurophysiologically plausible mechanisms for extraction of salient regions.
