Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks

2017 ◽  
Vol 22 (2) ◽  
pp. 363-381 ◽  
Author(s):  
Wei Xu ◽  
Qili Wang ◽  
Runyu Chen
Keyword(s):  
Neural Networks ◽  
Emergency Management ◽  
Disease Severity ◽  
Recurrent Neural Networks ◽  
Temporal Prediction ◽  
Crop Disease ◽  
Spatio Temporal

Strategies for supplementing recurrent neural network training for spatio-temporal prediction

2019 ◽  
Vol 67 (7) ◽  
pp. 545-556 ◽  
Author(s):  
Mark Schutera ◽  
Stefan Elser ◽  
Jochen Abhau ◽  
Ralf Mikut ◽  
Markus Reischl
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
Autonomous Driving ◽  
Temporal Data ◽  
Generative Adversarial Network ◽  
Temporal Prediction ◽  
Adversarial Network ◽  
Augmentation Strategy ◽  
Spatio Temporal

Abstract In autonomous driving, prediction tasks address complex spatio-temporal data. This article describes the examination of Recurrent Neural Networks (RNNs) for object trajectory prediction in the image space. The proposed methods enhance the performance and spatio-temporal prediction capabilities of Recurrent Neural Networks. Two different data augmentation strategies and a hyperparameter search are implemented for this purpose. A conventional data augmentation strategy and a Generative Adversarial Network (GAN) based strategy are analyzed with respect to their ability to close the generalization gap of Recurrent Neural Networks. The results are then discussed using single-object tracklets provided by the KITTI Tracking Dataset. This work demonstrates the benefits of augmenting spatio-temporal data with GANs.

scholarly journals Spatio-Temporal Prediction of Baltimore Crime Events Using CLSTM Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 209101-209112
Author(s):  
Nicolas Esquivel ◽  
Orietta Nicolis ◽  
Billy Peralta ◽  
Jorge Mateu
Keyword(s):  
Neural Networks ◽  
Temporal Prediction ◽  
Spatio Temporal

scholarly journals Localized Convolutional Neural Networks for Geospatial Wind Forecasting

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3440
Author(s):  
Arnas Uselis ◽  
Mantas Lukoševičius ◽  
Lukas Stasytis
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Prediction Models ◽  
State Of The Art ◽  
Translation Invariance ◽  
Public Repository ◽  
Temporal Prediction ◽  
Prediction Test ◽  
Data Translation ◽  
Spatio Temporal

Convolutional Neural Networks (CNN) possess many positive qualities when it comes to spatial raster data. Translation invariance enables CNNs to detect features regardless of their position in the scene. However, in some domains, like geospatial, not all locations are exactly equal. In this work, we propose localized convolutional neural networks that enable convolutional architectures to learn local features in addition to the global ones. We investigate their instantiations in the form of learnable inputs, local weights, and a more general form. They can be added to any convolutional layers, easily end-to-end trained, introduce minimal additional complexity, and let CNNs retain most of their benefits to the extent that they are needed. In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets. For one of them, we propose a method to spatially order the unordered data. We compare the recent state-of-the-art spatio-temporal prediction models on the same data. Models that use convolutional layers can be and are extended with our localizations. In all these cases our extensions improve the results, and thus often the state-of-the-art. We share all the code at a public repository.

scholarly journals A MANY-TO-MANY FULLY CONVOLUTIONAL RECURRENT NETWORK FOR MULTITEMPORAL CROP RECOGNITION

2019 ◽  
Vol IV-2/W7 ◽  
pp. 25-32 ◽  
Author(s):  
J. A. Chamorro ◽  
J. D. Bermudez ◽  
P. N. Happ ◽  
R. Q. Feitosa
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Temporal Dynamics ◽  
Agricultural Practices ◽  
Remote Sensing Data ◽  
Recurrent Network ◽  
Data Sets ◽  
Tropical Regions ◽  
Spatio Temporal ◽  
Multitemporal Remote Sensing

<p><strong>Abstract.</strong> Recently, recurrent neural networks have been proposed for crop mapping from multitemporal remote sensing data. Most of these proposals have been designed and tested in temperate regions, where a single harvest per season is the rule. In tropical regions, the favorable climate and local agricultural practices, such as crop rotation, result in more complex spatio-temporal dynamics, where the single harvest per season assumption does not hold. In this context, a demand arises for methods capable of recognizing agricultural crops at multiple dates along the multitemporal sequence. In the present work, we propose to adapt two recurrent neural networks, originally conceived for single harvest per season, for multidate crop recognition. In addition, we propose a novel multidate approach based on bidirectional fully convolutional recurrent neural networks. These three architectures were evaluated on public Sentinel-1 data sets from two tropical regions in Brazil. In our experiments, all methods achieved state-of-the-art accuracies with a clear superiority of the proposed architecture. It outperformed its counterparts in up to 3.8% and 7.4%, in terms of per-month overall accuracy, and it was the best performing method in terms of F1-score for most crops and dates on both regions.</p>

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