Monthly mapping of forest harvesting using dense time series Sentinel-1 SAR imagery and deep learning

The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or harvest over agricultural lands, clearance of forested areas, and/or modifications on the urban landscape. This study, therefore, incorporated historical SAR images to boost the detection of flood-induced changes during extreme weather events, using the Long Short-Term Memory (LSTM) method. Additionally, to incorporate the spatial signatures for the change detection, we applied a deep learning-based spatiotemporal simulation framework, Convolutional Long Short-Term Memory (ConvLSTM), for simulating a synthetic image using Sentinel One intensity time series. This synthetic image will be prepared in advance of flood events, and then it can be used to detect flood areas using change detection when the post-image is available. Practically, significant divergence between the synthetic image and post-image is expected over inundated zones, which can be mapped by applying thresholds to the Delta image (synthetic image minus post-image). We trained and tested our model on three events from Australia, Brazil, and Mozambique. The generated Flood Proxy Maps were compared against reference data derived from Sentinel Two and Planet Labs optical data. To corroborate the effectiveness of the proposed methods, we also generated Delta products for two baseline models (closest post-image minus pre-image and historical mean minus post-image) and two LSTM architectures: normal LSTM and ConvLSTM. Results show that thresholding of ConvLSTM Delta yielded the highest Cohen’s Kappa coefficients in all study cases: 0.92 for Australia, 0.78 for Mozambique, and 0.68 for Brazil. Lower Kappa values obtained in the Mozambique case can be subject to the topographic effect on SAR imagery. These results still confirm the benefits in terms of classification accuracy that convolutional operations provide in time series analysis of satellite data employing spatially correlated information in a deep learning framework.

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Deep Learning for text in limted data settings

10.36227/techrxiv.12100692 ◽

2020 ◽

Author(s):

Pathikkumar Patel ◽

Bhargav Lad ◽

Jinan Fiaidhi

Keyword(s):

Machine Learning ◽

Time Series ◽

Deep Learning ◽

Sentiment Analysis ◽

Transfer Learning ◽

Text Classification ◽

State Of The Art ◽

Time Series Forecasting ◽

Text Data ◽

Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Applied Sciences ◽

10.3390/app11156923 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6923

Author(s):

Rui Zhang ◽

Zhanzhong Tang ◽

Dong Luo ◽

Hongxia Luo ◽

Shucheng You ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Identification Accuracy ◽

Optical Remote Sensing ◽

Sar Image ◽

Single Time ◽

Growing Period ◽

Sensing Technology ◽

Sar Imagery ◽

Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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Load forecasting of refrigerated display cabinet based on CEEMD–IPSO–LSTM combined model

Open Physics ◽

10.1515/phys-2021-0043 ◽

2021 ◽

Vol 19 (1) ◽

pp. 360-374

Author(s):

Yuan Pei ◽

Lei Zhenglin ◽

Zeng Qinghui ◽

Wu Yixiao ◽

Lu Yanli ◽

...

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Data ◽

Load Forecasting ◽

Series Data ◽

Forecasting Model ◽

Combined Model ◽

Forecasting Accuracy ◽

Forecasting Method ◽

Consumption Reduction

Abstract The load of the showcase is a nonlinear and unstable time series data, and the traditional forecasting method is not applicable. Deep learning algorithms are introduced to predict the load of the showcase. Based on the CEEMD–IPSO–LSTM combination algorithm, this paper builds a refrigerated display cabinet load forecasting model. Compared with the forecast results of other models, it finally proves that the CEEMD–IPSO–LSTM model has the highest load forecasting accuracy, and the model’s determination coefficient is 0.9105, which is obviously excellent. Compared with other models, the model constructed in this paper can predict the load of showcases, which can provide a reference for energy saving and consumption reduction of display cabinet.

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Deep learning models for forecasting aviation demand time series

Neural Computing and Applications ◽

10.1007/s00521-021-06232-y ◽

2021 ◽

Author(s):

Andreas Kanavos ◽

Fotios Kounelis ◽

Lazaros Iliadis ◽

Christos Makris

Keyword(s):

Time Series ◽

Deep Learning ◽

Learning Models

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Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽

10.3390/electronics10101151 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1151

Author(s):

Carolina Gijón ◽

Matías Toril ◽

Salvador Luna-Ramírez ◽

María Luisa Marí-Altozano ◽

José María Ruiz-Avilés

Keyword(s):

Time Series ◽

Deep Learning ◽

Time Series Analysis ◽

Data Traffic ◽

Medium Term ◽

Short Term ◽

Traffic Forecasting ◽

Series Analysis ◽

Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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