scholarly journals Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework

2020 ◽  
Vol 9 (9) ◽  
pp. 527
Author(s):  
Jiantao Liu ◽  
Quanlong Feng ◽  
Ying Wang ◽  
Bayartungalag Batsaikhan ◽  
Jianhua Gong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Local Governments ◽  
Learning Strategy ◽  
Future Research ◽  
Urban Landscapes ◽  
Plastic Mulch ◽  
Urban Green ◽  
Learning Framework ◽  
Multi Scale

With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research.

scholarly journals Investigation of The Sufficiency of Antalya Parks

2022 ◽  
Vol 9 (sp) ◽  
pp. 2449-2457
Author(s):  
Orhun Soydan ◽  
Nefise Çetin
Keyword(s):  
Local Governments ◽  
Social Needs ◽  
Green Spaces ◽  
Urban Landscapes ◽  
Spatial Analyses ◽  
Urban Green ◽  
Urban Green Spaces ◽  
Functional Value ◽  
Neighborhood Boundaries ◽  
Nature Parks

Urban green spaces are areas established to meet the recreational needs of urban people. Although green spaces vary from country to country and region in terms of plan and design features, they were basically created to allow people to meet with nature. Parks are the basic components of urban landscapes that provide environmental and social functional value. Urban parks, in particular, provide spaces for outdoor physical activities. In order to take advantage of the opportunities of activities in the parks, users must have convenient access to these resources. One of the most important aspects for researching the use and potential benefits of urban green spaces is the assessment of their geographic accessibility. The widespread use of smart city systems and the gradual expansion of their usage areas increase the importance of spatial analysis. Spatial analyses are used in today’s urban management in the processes of determining social needs, identifying current problems, and putting forward solutions. When spatial analyses are used together with GIS, the field of application develops even more, and it supports local governments in responding to the changing demands of the society for a better life. In the study, the adequacy and accessibility of 160 city parks in Konyaaltı District of Antalya Province were examined. In terms of the adequacy of the parks, the area value of 10 m2 per person determined with the Construction Plan numbered 3194 was taken as basis. In terms of accessibility, distance values of 200, 400, 800, 1,200 meters were examined. Neighborhood boundaries and population information were obtained from the relevant units, and Arc-GIS software was used in the analysis. It was determined that the parks in Konyaaltı district were insufficient in terms of adequacy and accessibility. Finally, suggestions were made in terms of increasing the adequacy of the parks and ensuring accessibility.

scholarly journals When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework

2020 ◽  
Vol 12 (20) ◽  
pp. 3276 ◽  
Author(s):  
Zhicheng Zhao ◽  
Ze Luo ◽  
Jian Li ◽  
Can Chen ◽  
Yingchao Piao
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Remote Sensing Image ◽  
Multitask Learning ◽  
Natural Image ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Learning Framework ◽  
Continuous Progress ◽  
Image Datasets

In recent years, the development of convolutional neural networks (CNNs) has promoted continuous progress in scene classification of remote sensing images. Compared with natural image datasets, however, the acquisition of remote sensing scene images is more difficult, and consequently the scale of remote sensing image datasets is generally small. In addition, many problems related to small objects and complex backgrounds arise in remote sensing image scenes, presenting great challenges for CNN-based recognition methods. In this article, to improve the feature extraction ability and generalization ability of such models and to enable better use of the information contained in the original remote sensing images, we introduce a multitask learning framework which combines the tasks of self-supervised learning and scene classification. Unlike previous multitask methods, we adopt a new mixup loss strategy to combine the two tasks with dynamic weight. The proposed multitask learning framework empowers a deep neural network to learn more discriminative features without increasing the amounts of parameters. Comprehensive experiments were conducted on four representative remote sensing scene classification datasets. We achieved state-of-the-art performance, with average accuracies of 94.21%, 96.89%, 99.11%, and 98.98% on the NWPU, AID, UC Merced, and WHU-RS19 datasets, respectively. The experimental results and visualizations show that our proposed method can learn more discriminative features and simultaneously encode orientation information while effectively improving the accuracy of remote sensing scene classification.

scholarly journals An Impartial Semi-Supervised Learning Strategy for Imbalanced Classification on VHR Images

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6699
Author(s):  
Fei Sun ◽  
Fang Fang ◽  
Run Wang ◽  
Bo Wan ◽  
Qinghua Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Supervised Learning ◽  
Learning Strategy ◽  
Gradient Boosting ◽  
Support Vector ◽  
Imbalanced Learning ◽  
Learning Methods ◽  
Minority Class ◽  
Imbalanced Classification ◽  
Extreme Gradient Boosting

Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.

scholarly journals A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters

2016 ◽  
Vol 89 (10) ◽  
pp. 893-902 ◽  
Author(s):  
Yue Huang ◽  
Chi Liu ◽  
John F. Eisses ◽  
Sohail Z. Husain ◽  
Gustavo K. Rohde
Keyword(s):  
Supervised Learning ◽  
Pancreatic Islet ◽  
Texture Features ◽  
Learning Framework ◽  
Multi Scale ◽  
Color Texture

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review

2020 ◽  
Vol 27 (5) ◽  
pp. 385-391
Author(s):  
Lin Zhong ◽  
Zhong Ming ◽  
Guobo Xie ◽  
Chunlong Fan ◽  
Xue Piao
Keyword(s):  
Supervised Learning ◽  
Protein Interactions ◽  
Computational Models ◽  
Prediction Models ◽  
Chromatin Modification ◽  
Computational Prediction ◽  
Human Diseases ◽  
Future Research ◽  
Non Coding Rna ◽  
Long Non Coding Rna

: In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological processes, especially in RNA progressing, chromatin modification, and cell differentiation, as well as many other processes. Surprisingly, lncRNA has an inseparable relationship with human diseases such as cancer. Therefore, only by knowing more about the function of lncRNA can we better solve the problems of human diseases. However, lncRNAs need to bind to proteins to perform their biomedical functions. So we can reveal the lncRNA function by studying the relationship between lncRNA and protein. But due to the limitations of traditional experiments, researchers often use computational prediction models to predict lncRNA protein interactions. In this review, we summarize several computational models of the lncRNA protein interactions prediction base on semi-supervised learning during the past two years, and introduce their advantages and shortcomings briefly. Finally, the future research directions of lncRNA protein interaction prediction are pointed out.

scholarly journals Mulching as water-saving technique in dryland agriculture: review article

2019 ◽  
Vol 43 (1) ◽  
Author(s):  
Mohammad Abdul Kader ◽  
Ashutus Singha ◽  
Mili Amena Begum ◽  
Arif Jewel ◽  
Ferdous Hossain Khan ◽  
...  
Keyword(s):  
Arid Regions ◽  
Review Article ◽  
Water Saving ◽  
Future Research ◽  
Agricultural Water ◽  
Plastic Mulch ◽  
Dryland Agriculture ◽  
Crop Cultivation ◽  
Agricultural Water Resources ◽  
Semi Arid

Abstract Agricultural water resources have been limited over the years due to global warming and irregular rainfall in the arid and semi-arid regions. To mitigate the water stress in agriculture, mulching has a crucial impact as a water-saving technique in rain-fed crop cultivation. It is important mainly for preserving soil moisture, relegating soil temperature, and limiting soil evaporation, which affects the crop yield. Mulching has many strategic effects on soil ecosystem, crop growth, and climate. Mulch insulates the soil, helping to provide a buffer from cold and hot temperatures that have a crucial activity in creating beautiful and protected landscapes. This study has accumulated a series of information about both organic and plastic mulch materials and its applicability on crop cultivation. Moreover, future research potentials of mulching with modeling were discussed to quantify water loss in agriculture.

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