scholarly journals Mapping and Discriminating Rural Settlements Using Gaofen-2 Images and a Fully Convolutional Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6062
Author(s):  
Ziran Ye ◽  
Bo Si ◽  
Yue Lin ◽  
Qiming Zheng ◽  
Ran Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Rural Areas ◽  
Ground Truth ◽  
Representation Learning ◽  
Rural Settlement ◽  
Spatial Characteristic ◽  
Rural Settlements ◽  
Convolutional Network ◽  
Essential Information ◽  
Multi Scale

New ongoing rural construction has resulted in an extensive mixture of new settlements with old ones in the rural areas of China. Understanding the spatial characteristic of these rural settlements is of crucial importance as it provides essential information for land management and decision-making. Despite a great advance in High Spatial Resolution (HSR) satellite images and deep learning techniques, it remains a challenging task for mapping rural settlements accurately because of their irregular morphology and distribution pattern. In this study, we proposed a novel framework to map rural settlements by leveraging the merits of Gaofen-2 HSR images and representation learning of deep learning. We combined a dilated residual convolutional network (Dilated-ResNet) and a multi-scale context subnetwork into an end-to-end architecture in order to learn high resolution feature representations from HSR images and to aggregate and refine the multi-scale features extracted by the aforementioned network. Our experiment in Tongxiang city showed that the proposed framework effectively mapped and discriminated rural settlements with an overall accuracy of 98% and Kappa coefficient of 85%, achieving comparable and improved performance compared to other existing methods. Our results bring tangible benefits to support other convolutional neural network (CNN)-based methods in accurate and timely rural settlement mapping, particularly when up-to-date ground truth is absent. The proposed method does not only offer an effective way to extract rural settlement from HSR images but open a new opportunity to obtain spatial-explicit understanding of rural settlements.

scholarly journals Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

2021 ◽  
Vol 13 (12) ◽  
pp. 2425
Author(s):  
Yiheng Cai ◽  
Dan Liu ◽  
Jin Xie ◽  
Jingxian Yang ◽  
Xiangbin Cui ◽  
...  
Keyword(s):  
Deep Learning ◽  
Global Climate ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Sheet Thickness ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Ice Surface ◽  
Layer Extraction

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

Abstract 20: Deep Learning-Based Automated Intracranial Hemorrhage Detection and Notification

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Benjamin Zahneisen ◽  
Matus Straka ◽  
Shalini Bammer ◽  
Greg Albers ◽  
Roland Bammer
Keyword(s):  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Intracranial Hemorrhage ◽  
Health Care Professionals ◽  
Gold Standard ◽  
Ground Truth ◽  
Mobile App ◽  
Training Dataset ◽  
Convolutional Network ◽  
Ground Truth Data

Introduction: Ruling out hemorrhage (stroke or traumatic) prior to administration of thrombolytics is critical for Code Strokes. A triage software that identifies hemorrhages on head CTs and alerts radiologists would help to streamline patient care and increase diagnostic confidence and patient safety. ML approach: We trained a deep convolutional network with a hybrid 3D/2D architecture on unenhanced head CTs of 805 patients. Our training dataset comprised 348 positive hemorrhage cases (IPH=245, SAH=67, Sub/Epi-dural=70, IVH=83) (128 female) and 457 normal controls (217 female). Lesion outlines were drawn by experts and stored as binary masks that were used as ground truth data during the training phase (random 80/20 train/test split). Diagnostic sensitivity and specificity were defined on a per patient study level, i.e. a single, binary decision for presence/absence of a hemorrhage on a patient’s CT scan. Final validation was performed in 380 patients (167 positive). Tool: The hemorrhage detection module was prototyped in Python/Keras. It runs on a local LINUX server (4 CPUs, no GPUs) and is embedded in a larger image processing platform dedicated to stroke. Results: Processing time for a standard whole brain CT study (3-5mm slices) was around 2min. Upon completion, an instant notification (by email and/or mobile app) was sent to users to alert them about the suspected presence of a hemorrhage. Relative to neuroradiologist gold standard reads the algorithm’s sensitivity and specificity is 90.4% and 92.5% (95% CI: 85%-94% for both). Detection of acute intracranial hemorrhage can be automatized by deploying deep learning. It yielded very high sensitivity/specificity when compared to gold standard reads by a neuroradiologist. Volumes as small as 0.5mL could be detected reliably in the test dataset. The software can be deployed in busy practices to prioritize worklists and alert health care professionals to speed up therapeutic decision processes and interventions.

scholarly journals Exploitation of deep learning in the automatic detection of cracks on paved roads

GEOMATICA ◽  
2019 ◽  
Vol 73 (2) ◽  
pp. 29-44
Author(s):  
Won Mo Jung ◽  
Faizaan Naveed ◽  
Baoxin Hu ◽  
Jianguo Wang ◽  
Ningyuan Li
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Networks ◽  
Test Image ◽  
Convolutional Network ◽  
Image Patches ◽  
Severity Levels ◽  
First Time ◽  
Different Levels

With the advance of deep learning networks, their applications in the assessment of pavement conditions are gaining more attention. A convolutional neural network (CNN) is the most commonly used network in image classification. In terms of pavement assessment, most existing CNNs are designed to only distinguish between cracks and non-cracks. Few networks classify cracks in different levels of severity. Information on the severity of pavement cracks is critical for pavement repair services. In this study, the state-of-the-art CNN used in the detection of pavement cracks was improved to localize the cracks and identify their distress levels based on three categories (low, medium, and high). In addition, a fully convolutional network (FCN) was, for the first time, utilized in the detection of pavement cracks. These designed architectures were validated using the data acquired on four highways in Ontario, Canada, and compared with the ground truth that was provided by the Ministry of Transportation of Ontario (MTO). The results showed that with the improved CNN, the prediction precision on a series of test image patches were 72.9%, 73.9%, and 73.1% for cracks with the severity levels of low, medium, and high, respectively. The precision for the FCN was tested on whole pavement images, resulting in 62.8%, 63.3%, and 66.4%, respectively, for cracks with the severity levels of low, medium, and high. It is worth mentioning that the ground truth contained some uncertainties, which partially contributed to the relatively low precision.

scholarly journals Automated and accurate segmentation of leaf venation networks via deep learning

2020 ◽  
Author(s):  
H. Xu ◽  
B. Blonder ◽  
M. Jodra ◽  
Y. Malhi ◽  
M.D. Fricker
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Spatial Scales ◽  
Ground Truth ◽  
Extraction Methods ◽  
List Type ◽  
Network Graph ◽  
Leaf Venation ◽  
Leaf Vein ◽  
Multi Scale

SummaryLeaf vein network geometry can predict levels of resource transport, defence, and mechanical support that operate at different spatial scales. However, it is challenging to quantify network architecture across scales, due to the difficulties both in segmenting networks from images, and in extracting multi-scale statistics from subsequent network graph representations.Here we develop deep learning algorithms using convolutional neural networks (CNNs) to automatically segment leaf vein networks. Thirty-eight CNNs were trained on subsets of manually-defined ground-truth regions from >700 leaves representing 50 southeast Asian plant families. Ensembles of 6 independently trained CNNs were used to segment networks from larger leaf regions (~100 mm2). Segmented networks were analysed using hierarchical loop decomposition to extract a range of statistics describing scale transitions in vein and areole geometry.The CNN approach gave a precision-recall harmonic mean of 94.5% ± 6%, outperforming other current network extraction methods, and accurately described the widths, angles, and connectivity of veins. Multi-scale statistics then enabled identification of previously-undescribed variation in network architecture across species.We provide a LeafVeinCNN software package to enable multi-scale quantification of leaf vein networks, facilitating comparison across species and exploration of the functional significance of different leaf vein architectures.

scholarly journals PRACTICAL PROBLEMS OF URBAN PLANNING OF RURAL AREAS ON THE EXAMPLE OF THE MO " ZORKALTSEVSKOYE RURAL SETTLEMENT»

2021 ◽  
Vol 3 (2) ◽  
pp. 140-144
Author(s):  
Daria E. Paltseva
Keyword(s):  
Urban Planning ◽  
Rural Areas ◽  
Information Resources ◽  
Rural Settlement ◽  
Rural Settlements ◽  
Territorial Planning

The relevance of the work is due to the fact that today in rural settlements there is a problem when making changes to urban planning documentation in various information resources. Purpose: analysis of territorial planning documents of the Ministry of Defense "Zorkaltsevskoye rural settlement".

scholarly journals Multi-Source and Multi-Scale Platform for Quantitative Assessment of Shallow or/and Coastal Qater

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 20
Author(s):  
Anna Brook ◽  
Ran Reznikov ◽  
Martin Kanning ◽  
Thomas Jarmer
Keyword(s):  
Learning Process ◽  
Coastal Waters ◽  
Super Resolution ◽  
Test Site ◽  
Alternative Methods ◽  
Spatial Characteristic ◽  
Convolutional Network ◽  
Training Site ◽  
Inland Water ◽  
Multi Scale

Coastal waters are one of the most vulnerable resources that require comprehensive investigation in space and time. One of the key factors for effective coastal monitoring is the use of remote sensing technologies. Since the Coastal Zone Color Scanner (CZCS) in 1978, a long list of space-borne missions had been successfully launched. However, those missions are limited to coastal waters applications. Despite a large number of missions, the existing systems are still facing similar challenges as four decades ago. Spatial and spectral data reconstruction and recovery a high resolution (HR) imagery data from a low resolution (LR) imaging is a challenging task in many applications. The most promising technique in the field of digital image processing is known as Super Resolution (SR). Many techniques focus on reconstructing information at the sub-pixel level and dividing the original LR space into pixels corresponding to the HR space. Other methods assume that a series of LR images (in time) of a scene scanned from different perspectives (affine) will provide SR. Alternative methods use different data sources and proper image algorithms. In most cases, SR methods will perform a learning process in which the system will try to identify the inherent redundancy in the natural data in order to retrieve HR information from LR based on a spatial correlation between the original images. The learning process can be significantly efficient by using the Convolutional Neural Network (CNN). CNN submit to training through a large dataset that preserves the scene’s characteristics. The flexibility afforded by CNN is learning nonlinear relationships when reconstructing a spatial characteristic from an LR image to HR image. The main aim of this study is to identify spectral features related to the coastal water and inland water variations at different spatial and temporal scale and integrate them with a multi-scale information system. The main objectives of the study are developing of spatial-temporal-spectral fusion approach for multi-source data collected from the same geographical site; creating a new method for single image reconstruction from non-complementary information scene. The proposed method measures HR given LR by a downscaling process by turning HR into an LR. The deterministic process calculated using a Gaussian filter and by a photographic-focused distribution function. The correlation coefficient (at the LR-pixel level) used as an inverse ratio to upscaling. The proposed architecture is based on a three-convolutional network. In the first stage, the convolution is directly applied to the LR data, and then another sub-pixel convolution layer is subtracted to generate SR data from LR data through an upscaling process. This study performed in two sites, (1) a training site in Israel, (2) a test site in Germany. The training site is shallow seawaters around Oren River, Israel and the test site is Alfsee inland water in Germany. The results in both sites are SR imagery with full Sentinel 2 spectral resolution and spatial resolution of 0.3 m.

scholarly journals Self-Organization of Citizens at the Rural Settlements – Look Through Pink Glasses

2020 ◽  
Vol 8 (2) ◽  
pp. 114-132
Author(s):  
Elena S. Shomina
Keyword(s):  
Rural Areas ◽  
Agricultural Land ◽  
Local Level ◽  
Social Practices ◽  
Rural Settlement ◽  
Self Organization ◽  
Rural Settlements ◽  
Vladimir Region ◽  
Changing Attitudes

For more than 30 years, Russia has been developing “territorial public self-government” – the process of self-organization of citizens at the local level (TOS in Russian). The article considers the development of TOS as a tool for involvement and participation of citizens in local self-government (LSG). In Russia at the beginning of 2020, there were more than 33 thousand TOS, half of them in rural areas. It is a slow transformation of TOS activities (from the distribution of humanitarian aid in the 1990s) before participating in National projects after 2018), as well as changing attitudes towards TOS (from lack of recognition and support – to cooperation and allocation of serious funds, up to presidential grants). On the basis of long-term included observation, positive social practices that are implemented in rural settlements, features of the daily activities of rural TOS, features of rural life, including the nature of development and individual consumption of municipal resources, environmental problems and the seizure of agricultural land and pastures are described. Rural TOS are forced to do more practical things, their projects are more labor-intensive, and the contribution of the residents themselves is more tangible and visible (engineering infrastructure, roads and sidewalks, gasification, electricity and street lighting, garbage collection, and other cultural and leisure projects): a different scale than in the city, but much greater diversity, involvement and initiative of the residents themselves. The positive experience of the TOS of Kameshkovsky rural settlement of the Vladimir region and Novopavlovsky rural settlement of the Krasnodar territory is considered. Numerous social practices are described, as well as the problems encountered in connection with the emergence of municipal districts.

Extracting Rural Settlement Information from Quickbird Images

2012 ◽  
Vol 500 ◽  
pp. 450-457
Author(s):  
Cun Jian Yang ◽  
Zhen Luo
Keyword(s):  
Rural Areas ◽  
Spectral Feature ◽  
Spatial Relationship ◽  
Texture Feature ◽  
Rural Settlement ◽  
Geometric Feature ◽  
Image Objects ◽  
Multi Scale ◽  
Sensing Technology ◽  
Image Object

Acquiring the information for rural settlement timely and accurately has an important significance for construction and development of rural areas. The development of remote sensing technology provides advanced means of the acquirement of the information of settlement. The study of extracting rural settlement information from Quickbird images in Xindu district, Chengdu City, P.R.of China was discussed here. Firstly, The Quickbird images such as panchromatic image and multi-spectral images were processed by geometric correction, enhancement and fusion. Secondly, the homogeneous image objects were formed by using multi-scale segmentation technology based on knowledge. Thirdly, the features such spectral feature, spatial relationship feature, texture feature and geometric feature of the image objects were obtained for each image object by using feature calculation. Fourthly, the feature knowledge of rural settlement unit and its component were obtained by using knowledge discovering. Finally, the rural settlement unit and its component information were extracted by matching the features with the feature knowledge of rural settlement unit and its component based on reasoning. It was shown that the rural settlement unit and its component information can be effectively extracted from Quickbird images by using our proposed method in this paper.

scholarly journals Antimicrobial peptide identification using multi-scale convolutional network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Xin Su ◽  
Jing Xu ◽  
Yanbin Yin ◽  
Xiongwen Quan ◽  
Han Zhang
Keyword(s):  
Deep Learning ◽  
Antimicrobial Peptide ◽  
State Of The Art ◽  
Peptide Identification ◽  
The State ◽  
Fusion Model ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Peptide Database

Abstract Background Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN.

scholarly journals Newly Built Construction Detection in SAR Images Using Deep Learning

2019 ◽  
Vol 11 (12) ◽  
pp. 1444 ◽  
Author(s):  
Raveerat Jaturapitpornchai ◽  
Masashi Matsuoka ◽  
Naruo Kanemoto ◽  
Shigeki Kuzuoka ◽  
Riho Ito ◽  
...  
Keyword(s):  
Deep Learning ◽  
Remote Sensing Data ◽  
Ground Truth ◽  
Weather Conditions ◽  
Google Earth ◽  
Sar Images ◽  
Convolutional Network ◽  
Optical Images ◽  
Sar Data ◽  
Deep Learning Network

Remote sensing data can be utilized to help developing countries monitor the use of land. However, the problem of constant cloud coverage prevents us from taking full advantage of satellite optical images. Therefore, we instead opt to use data from synthetic-aperture radar (SAR), which can capture images of the Earth’s surface regardless of the weather conditions. In this study, we use SAR data to identify newly built constructions. Most studies on change detection tend to detect all of the changes that have a similar temporal change characteristic occurring on two occasions, while we want to identify only the constructions and avoid detecting other changes such as the seasonal change of vegetation. To do so, we study various deep learning network techniques and have decided to propose the fully convolutional network with a skip connection. We train this network with pairs of SAR data acquired on two different occasions from Bangkok and the ground truth, which we manually create from optical images available from Google Earth for all of the SAR pairs. Experiments to assign the most suitable patch size, loss weighting, and epoch number to the network are discussed in this paper. The trained model can be used to generate a binary map that indicates the position of these newly built constructions precisely with the Bangkok dataset, as well as with the Hanoi and Xiamen datasets with acceptable results. The proposed model can even be used with SAR images of the same specific satellite from another orbit direction and still give promising results.

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