Image2Weather: A Large-Scale Image Dataset for Weather Property Estimation

2016 ◽  
Author(s):  
Wei-Ta Chu ◽  
Xiang-You Zheng ◽  
Ding-Shiuan Ding
Keyword(s):  
Large Scale ◽  
Property Estimation ◽  
Image Dataset

Using relative geologic time to constrain CNN-based seismic interpretation and property estimation

Geophysics ◽  
2021 ◽  
pp. 1-44
Author(s):  
Aria Abubakar ◽  
Haibin Di ◽  
Zhun Li
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Seismic Interpretation ◽  
Seismic Survey ◽  
Geologic Time ◽  
Seismic Amplitude ◽  
Property Estimation ◽  
Constrained Learning ◽  
Seismic Amplitudes ◽  
Multiplicative Regularization

Three-dimensional seismic interpretation and property estimation is essential to subsurface mapping and characterization, in which machine learning, particularly supervised convolutional neural network (CNN) has been extensively implemented for improved efficiency and accuracy in the past years. In most seismic applications, however, the amount of available expert annotations is often limited, which raises the risk of overfitting a CNN particularly when only seismic amplitudes are used for learning. In such a case, the trained CNN would have poor generalization capability, causing the interpretation and property results of obvious artifacts, limited lateral consistency and thus restricted application to following interpretation/modeling procedures. This study proposes addressing such an issue by using relative geologic time (RGT), which explicitly preserves the large-scale continuity of seismic patterns, to constrain a seismic interpretation and/or property estimation CNN. Such constrained learning is enforced in twofold: (1) from the perspective of input, the RGT is used as an additional feature channel besides seismic amplitude; and more innovatively (2) the CNN has two output branches, with one for matching the target interpretation or properties and the other for reconstructing the RGT. In addition is the use of multiplicative regularization to facilitate the simultaneous minimization of the target-matching loss and the RGT-reconstruction loss. The performance of such an RGT-constrained CNN is validated by two examples, including facies identification in the Parihaka dataset and property estimation in the F3 Netherlands dataset. Compared to those purely from seismic amplitudes, both the facies and property predictions with using the proposed RGT constraint demonstrate significantly reduced artifacts and improved lateral consistency throughout a seismic survey.

scholarly journals LSUN-Stanford Car Dataset: Enhancing Large-Scale Car Image Datasets Using Deep Learning for Usage in GAN Training

2020 ◽  
Vol 10 (14) ◽  
pp. 4913
Author(s):  
Tin Kramberger ◽  
Božidar Potočnik
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Image Dataset ◽  
Image Datasets

Currently there is no publicly available adequate dataset that could be used for training Generative Adversarial Networks (GANs) on car images. All available car datasets differ in noise, pose, and zoom levels. Thus, the objective of this work was to create an improved car image dataset that would be better suited for GAN training. To improve the performance of the GAN, we coupled the LSUN and Stanford car datasets. A new merged dataset was then pruned in order to adjust zoom levels and reduce the noise of images. This process resulted in fewer images that could be used for training, with increased quality though. This pruned dataset was evaluated by training the StyleGAN with original settings. Pruning the combined LSUN and Stanford datasets resulted in 2,067,710 images of cars with less noise and more adjusted zoom levels. The training of the StyleGAN on the LSUN-Stanford car dataset proved to be superior to the training with just the LSUN dataset by 3.7% using the Fréchet Inception Distance (FID) as a metric. Results pointed out that the proposed LSUN-Stanford car dataset is more consistent and better suited for training GAN neural networks than other currently available large car datasets.

Understanding the cultural concerns of libraries based on automatic image analysis

2019 ◽  
Vol 37 (3) ◽  
pp. 419-434
Author(s):  
Heng Ding ◽  
Wei Lu ◽  
Tingting Jiang
Keyword(s):  
Exploratory Study ◽  
Design Methodology ◽  
Large Scale ◽  
State Of The Art ◽  
Automatic Analysis ◽  
Automatic Image Analysis ◽  
Content Type ◽  
Image Dataset ◽  
Learning Technique ◽  
Semantic Concepts

Purpose Photographs are a kind of cultural heritage and very useful for cultural and historical studies. However, traditional or manual research methods are costly and cannot be applied on a large scale. This paper aims to present an exploratory study for understanding the cultural concerns of libraries based on the automatic analysis of large-scale image collections. Design/methodology/approach In this work, an image dataset including 85,023 images preserved and shared by 28 libraries is collected from the Flickr Commons project. Then, a method is proposed for representing the culture with a distribution of visual semantic concepts using a state-of-the-art deep learning technique and measuring the cultural concerns of image collections using two metrics. Case studies on this dataset demonstrated the great potential and promise of the method for understanding large-scale image collections from the perspective of cultural concerns. Findings The proposed method has the ability to discover important cultural units from large-scale image collections. The proposed two metrics are able to quantify the cultural concerns of libraries from different perspectives. Originality/value To the best of the authors’ knowledge, this is the first automatic analysis of images for the purpose of understanding cultural concerns of libraries. The significance of this study mainly consists in the proposed method of understanding the cultural concerns of libraries based on the automatic analysis of the visual semantic concepts in image collections. Moreover, this paper has examined the cultural concerns (e.g. important cultural units, cultural focus, trends and volatility of cultural concerns) of 28 libraries.

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