scholarly journals Active fire detection in Landsat-8 imagery: A large-scale dataset and a deep-learning study

2021 ◽  
Vol 178 ◽  
pp. 171-186
Author(s):  
Gabriel Henrique de Almeida Pereira ◽  
Andre Minoro Fusioka ◽  
Bogdan Tomoyuki Nassu ◽  
Rodrigo Minetto
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Fire Detection ◽  
Landsat 8 ◽  
Learning Study ◽  
Active Fire ◽  
Large Scale Dataset

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3166
Author(s):  
Jash R. Parekh ◽  
Ate Poortinga ◽  
Biplov Bhandari ◽  
Timothy Mayer ◽  
David Saah ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Remotely Sensed Data ◽  
Statistical Indices ◽  
Deep Learning Neural Network

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

scholarly journals Maritime vessel re-identification: novel VR-VCA dataset and a multi-branch architecture MVR-net

2021 ◽  
Vol 32 (3) ◽  
Author(s):  
Amir Ghahremani ◽  
Tunc Alkanat ◽  
Egor Bondarev ◽  
Peter H. N. de With
Keyword(s):  
Deep Learning ◽  
Best Practices ◽  
Large Scale ◽  
Flow Analysis ◽  
Challenging Problem ◽  
Maritime Traffic ◽  
Large Scale Dataset ◽  
Identity Matching ◽  
Learning Architectures ◽  
Disjoint Camera Views

AbstractMaritime vessel re-identification (re-ID) is a computer vision task of vessel identity matching across disjoint camera views. Prominent applications of vessel re-ID exist in the fields of surveillance and maritime traffic flow analysis. However, the field suffers from the absence of a large-scale dataset that enables training of deep learning models. In this study, we present a new dataset that includes 4614 images of 729 vessels along with 5-bin orientation and 8-class vessel-type annotations to promote further research. A second contribution of this study is the baseline re-ID analysis of our new dataset. Performances of 10 recent deep learning architectures are quantitatively compared to reveal the best practices. Lastly, we propose a novel multi-branch deep learning architecture, Maritime Vessel Re-ID network (MVR-net), to address the challenging problem of vessel re-ID. Evaluation of our approach on the new dataset yields 74.5% mAP and 77.9% Rank-1 score, providing a performance increase of 5.7% mAP and 5.0% Rank-1 over the best-performing baseline. MVR-net also outperforms the PRN (a pioneering vehicle re-ID network), by 2.9% and 4.3% higher mAP and Rank-1, respectively.

scholarly journals Towards a Deep-Learning-Based Framework of Sentinel-2 Imagery for Automated Active Fire Detection

2021 ◽  
Vol 13 (23) ◽  
pp. 4790
Author(s):  
Qi Zhang ◽  
Linlin Ge ◽  
Ruiheng Zhang ◽  
Graciela Isabel Metternicht ◽  
Chang Liu ◽  
...  
Keyword(s):  
United States ◽  
Deep Learning ◽  
Fire Detection ◽  
The United States ◽  
Fire Service ◽  
Detection Accuracy ◽  
Processing Efficiency ◽  
High Resolution Data ◽  
Active Fire ◽  
Sentinel 2

This paper proposes an automated active fire detection framework using Sentinel-2 imagery. The framework is made up of three basic parts including data collection and preprocessing, deep-learning-based active fire detection, and final product generation modules. The active fire detection module is developed on a specifically designed dual-domain channel-position attention (DCPA)+HRNetV2 model and a dataset with semi-manually annotated active fire samples is constructed over wildfires that commenced on the east coast of Australia and the west coast of the United States in 2019–2020 for the training process. This dataset can be used as a benchmark for other deep-learning-based algorithms to improve active fire detection accuracy. The performance of active fire detection is evaluated regarding the detection accuracy of deep-learning-based models and the processing efficiency of the whole framework. Results indicate that the DCPA and HRNetV2 combination surpasses DeepLabV3 and HRNetV2 models for active fire detection. In addition, the automated framework can deliver active fire detection results of Sentinel-2 inputs with coverage of about 12,000 km2 (including data download) in less than 6 min, where average intersections over union (IoUs) of 70.4% and 71.9% were achieved in tests over Australia and the United States, respectively. Concepts in this framework can be further applied to other remote sensing sensors with data acquisitions in SWIR-NIR-Red ranges and can serve as a powerful tool to deal with large volumes of high-resolution data used in future fire monitoring systems and as a cost-efficient resource in support of governments and fire service agencies that need timely, optimized firefighting plans.

scholarly journals A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1133
Author(s):  
Zenun Kastrati ◽  
Lule Ahmedi ◽  
Arianit Kurti ◽  
Fatbardh Kadriu ◽  
Doruntina Murtezaj ◽  
...  
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Large Scale ◽  
Research Study ◽  
Low Resource ◽  
Analysis Task ◽  
Large Scale Dataset ◽  
Social Media Platforms ◽  
Experimental Findings

During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.

scholarly journals Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

2019 ◽  
Vol 38 (9) ◽  
pp. 2198-2210 ◽  
Author(s):  
Sarah Leclerc ◽  
Erik Smistad ◽  
Joao Pedrosa ◽  
Andreas Ostvik ◽  
Frederic Cervenansky ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
2D Echocardiography ◽  
Large Scale Dataset

scholarly journals Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System

2020 ◽  
Vol 13 (1) ◽  
pp. 9
Author(s):  
Herminarto Nugroho ◽  
Meredita Susanty ◽  
Ade Irawan ◽  
Muhamad Koyimatu ◽  
Ariana Yunita
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Detection System ◽  
Fire Detection ◽  
Features Extraction ◽  
Essential Information ◽  
Deep Learning Algorithm ◽  
Specific Direction ◽  
Variational Autoencoder

This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.

scholarly journals A New Deep Learning-Based Zero-Inflated Duration Model for Financial Data Irregularly Spaced in Time

2021 ◽  
Vol 9 ◽  
Author(s):  
Yong Shi ◽  
Wei Dai ◽  
Wen Long
Keyword(s):  
Deep Learning ◽  
Hybrid Model ◽  
Large Scale ◽  
Stock Exchange ◽  
Small Individual ◽  
Arrival Times ◽  
Large Scale Dataset ◽  
Duration Distribution ◽  
Zero Values ◽  
Individual Traders

In stock trading markets, trade duration (i. e., inter-arrival times of trades) usually exhibits high uncertainty and excessive zero values. To forecast conditional distribution of trade duration, this study proposes a hybrid model called “DL-ZIACD” for short, which addresses the problem of excessive zero values by a zero-inflated distribution. Meanwhile, dynamics of the distribution time-varying parameters are captured by a specially designed deep learning (DL) architecture in which the behavioral patterns of large traders and small individual traders are represented separately by different blocks. The proposed hybrid model takes advantage of the strong fitting ability of deep learning methods while allowing for providing a probabilistic output. This paper empirically applied the established model to a large-scale dataset, containing 9,900,000 transactions of the Chinese Shenzhen Stock Exchange 100 Index (SZSE 100) constituents. To the best of our knowledge, no previous studies have applied conditional duration models to a dataset of such a large scale. For both the central location forecasting and the extreme quantile forecasting, our proposed model exhibited significant superiority over the benchmark models, which indicates that our DL-ZIACD model can provide accurate forecasts in conditional duration distribution.

Sign in / Sign up

Export Citation Format

Share Document