scholarly journals The Temporal Dynamics of Slums Employing a CNN-Based Change Detection Approach

2019 ◽  
Vol 11 (23) ◽  
pp. 2844 ◽  
Author(s):  
Ruoyun Liu ◽  
Monika Kuffer ◽  
Claudio Persello
Keyword(s):  
Change Detection ◽  
Temporal Dynamics ◽  
Spatial Dynamics ◽  
Rapid Urbanization ◽  
Error Matrix ◽  
Global Challenge ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection Approach ◽  
Accuracy Difference

Along with rapid urbanization, the growth and persistence of slums is a global challenge. While remote sensing imagery is increasingly used for producing slum maps, only a few studies have analyzed their temporal dynamics. This study explores the potential of fully convolutional networks (FCNs) to analyze the temporal dynamics of small clusters of temporary slums using very high resolution (VHR) imagery in Bangalore, India. The study develops two approaches based on FCNs. The first approach uses a post-classification change detection, and the second trains FCNs to directly classify the dynamics of slums. For both approaches, the performances of 3 × 3 kernels and 5 × 5 kernels of the networks were compared. While classification results of individual years exhibit a relatively high F1-score (3 × 3 kernel) of 88.4% on average, the change accuracies are lower. The post-classification results obtained an F1-score of 53.8% and the change-detection networks obtained an F1-score of 53.7%. According to the trajectory error matrix (TEM), the post-classification results scored higher for the overall accuracy but lower for the accuracy difference of change trajectories than the change-detection networks. Although the two methods did not have significant differences in terms of accuracy, the change-detection network was less noisy. Within our study area, the areas of slums show a small overall decrease; the annual growth of slums (between 2012 and 2016) was 7173 m2, in contrast to an annual decline of 8390 m2. However, these numbers hid the spatial dynamics, which were much larger. Interestingly, areas where slums disappeared commonly changed into green areas, not into built-up areas. The proposed change-detection network provides a robust map of the locations of changes with lower confidence about the exact boundaries. This shows the potential of FCNs for detecting the dynamics of slums in VHR imagery.

scholarly journals Fully Convolutional Networks with Multiscale 3D Filters and Transfer Learning for Change Detection in High Spatial Resolution Satellite Images

2020 ◽  
Vol 12 (5) ◽  
pp. 799 ◽  
Author(s):  
Ahram Song ◽  
Jaewan Choi
Keyword(s):  
Change Detection ◽  
Transfer Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Semantic Segmentation ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Optimal Size ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

Remote sensing images having high spatial resolution are acquired, and large amounts of data are extracted from their region of interest. For processing these images, objects of various sizes, from very small neighborhoods to large regions composed of thousands of pixels, should be considered. To this end, this study proposes change detection method using transfer learning and recurrent fully convolutional networks with multiscale three-dimensional (3D) filters. The initial convolutional layer of the change detection network with multiscale 3D filters was designed to extract spatial and spectral features of materials having different sizes; the layer exploits pre-trained weights and biases of semantic segmentation network trained on an open benchmark dataset. The 3D filter sizes were defined in a specialized way to extract spatial and spectral information, and the optimal size of the filter was determined using highly accurate semantic segmentation results. To demonstrate the effectiveness of the proposed method, binary change detection was performed on images obtained from multi-temporal Korea multipurpose satellite-3A. Results revealed that the proposed method outperformed the traditional deep learning-based change detection methods and the change detection accuracy improved using multiscale 3D filters and transfer learning.

scholarly journals Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-Temporal Dynamics

2021 ◽  
pp. 102-117
Author(s):  
Antonio Alguacil ◽  
Wagner Gonçalves Pinto ◽  
Michael Bauerheim ◽  
Marc C. Jacob ◽  
Stéphane Moreau
Keyword(s):  
Boundary Conditions ◽  
Temporal Dynamics ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Spatio Temporal

scholarly journals Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5084
Author(s):  
Daliana Lobo Torres ◽  
Javier Noa Turnes ◽  
Pedro Juan Soto Vega ◽  
Raul Queiroz Feitosa ◽  
Daniel E. Silva ◽  
...  
Keyword(s):  
Change Detection ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Forest Change ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection Analysis ◽  
Sentinel 2

The availability of remote-sensing multisource data from optical-based satellite sensors has created new opportunities and challenges for forest monitoring in the Amazon Biome. In particular, change-detection analysis has emerged in recent decades to monitor forest-change dynamics, supporting some Brazilian governmental initiatives such as PRODES and DETER projects for biodiversity preservation in threatened areas. In recent years fully convolutional network architectures have witnessed numerous proposals adapted for the change-detection task. This paper comprehensively explores state-of-the-art fully convolutional networks such as U-Net, ResU-Net, SegNet, FC-DenseNet, and two DeepLabv3+ variants on monitoring deforestation in the Brazilian Amazon. The networks’ performance is evaluated experimentally in terms of Precision, Recall, F1-score, and computational load using satellite images with different spatial and spectral resolution: Landsat-8 and Sentinel-2. We also include the results of an unprecedented auditing process performed by senior specialists to visually evaluate each deforestation polygon derived from the network with the highest accuracy results for both satellites. This assessment allowed estimation of the accuracy of these networks simulating a process “in nature” and faithful to the PRODES methodology. We conclude that the high resolution of Sentinel-2 images improves the segmentation of deforestation polygons both quantitatively (in terms of F1-score) and qualitatively. Moreover, the study also points to the potential of the operational use of Deep Learning (DL) mapping as products to be consumed in PRODES.

scholarly journals Building Corner Detection in Aerial Images with Fully Convolutional Networks

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1915 ◽  
Author(s):  
Weigang Song ◽  
Baojiang Zhong ◽  
Xun Sun
Keyword(s):  
Conditional Random Fields ◽  
Structural Information ◽  
Scale Space ◽  
Corner Detection ◽  
Aerial Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection Approach ◽  
Corner Points ◽  
Fully Connected

In aerial images, corner points can be detected to describe the structural information of buildings for city modeling, geo-localization, and so on. For this specific vision task, the existing generic corner detectors perform poorly, as they are incapable of distinguishing corner points on buildings from those on other objects such as trees and shadows. Recently, fully convolutional networks (FCNs) have been developed for semantic image segmentation that are able to recognize a designated kind of object through a training process with a manually labeled dataset. Motivated by this achievement, an FCN-based approach is proposed in the present work to detect building corners in aerial images. First, a DeepLab model comprised of improved FCNs and fully-connected conditional random fields (CRFs) is trained end-to-end for building region segmentation. The segmentation is then further improved by using a morphological opening operation to increase its accuracy. Corner points are finally detected on the contour curves of building regions by using a scale-space detector. Experimental results show that the proposed building corner detection approach achieves an F-measure of 0.83 in the test image set and outperforms a number of state-of-the-art corner detectors by a large margin.

A Constant False Alarm Rate-(CFAR) Based Change Detection Approach to Helicopter Diagnostics

2008 ◽  
Author(s):  
Kenneth Ranney ◽  
Hiralal Khatri ◽  
Jerry Silvious ◽  
Kwok Tom ◽  
Romeo del Rosario
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Constant False Alarm Rate ◽  
Detection Approach

Spatial-temporal transmission dynamics and control of infectious diseases: Ebola virus disease (EVD) as a case study

2018 ◽  
Author(s):  
Cécile Viboud ◽  
Hélène Broutin ◽  
Gerardo Chowell
Keyword(s):  
Infectious Diseases ◽  
Disease Transmission ◽  
Ebola Virus ◽  
Temporal Dynamics ◽  
Virus Disease ◽  
Spatial Dynamics ◽  
Childhood Diseases ◽  
Middle Income ◽  
Theoretical Concepts ◽  
Dynamics And Control

Disentangling the spatial-temporal dynamics of infectious disease transmission is important to address issues of disease persistence, epidemic growth and optimal control. In this chapter, we review key concepts relating to the spatial-temporal dynamics of infectious diseases in meta-populations, whereby geographically separate subpopulations are connected by migration or mobility rates. We review the dynamics of colonization, persistence and extinction of emerging and recurrent pathogens in meta-populations; the role of demographic and environmental factors; and geographic heterogeneity in epidemic growth rate. We illustrate theoretical concepts by reviewing the spatial dynamics of childhood diseases and other acute infections in low- and middle-income countries, and provide a detailed description of the spatial-temporal dynamics of the 2014–16 Ebola epidemic in West Africa. We further discuss how increased availability of empirical data and recent methodological developments provide a deeper mechanistic understanding of transmission processes in space and time, and make recommendations for future work.

scholarly journals Automatic Deep Learning Semantic Segmentation of Ultrasound Thyroid Cineclips using Recurrent Fully Convolutional Networks

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Jeremy M. Webb ◽  
Duane D. Meixner ◽  
Shaheeda A. Adusei ◽  
Eric C. Polley ◽  
Mostafa Fatemi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks

scholarly journals Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 144
Author(s):  
Yuexing Han ◽  
Xiaolong Li ◽  
Bing Wang ◽  
Lu Wang
Keyword(s):  
Image Segmentation ◽  
Spatial Information ◽  
Medical Images ◽  
Tumor Segmentation ◽  
Convolutional Networks ◽  
Learning Framework ◽  
Fully Convolutional Networks ◽  
Segmentation Accuracy ◽  
Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

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