End-to-end Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping

Lei, Tao; Zhang, Qi; Xue, Dinghua; Chen, Tao; Meng, Hongying; Nandi, Asoke K.

doi:10.1109/icassp.2019.8682802

Cited by 31 publications

(16 citation statements)

References 18 publications

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“…In [10], the authors propose a supervised change detection architecture based on based on U-Nets [11]. Similarly, in [12], the authors propose another and better supervised architectures based on convolutional neural networks (CNN) and that shows very good performance to separate trivial changes from non-trivial ones.…”

Section: Supervised Methods For Change Detection and Damage Mappingmentioning

confidence: 99%

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

2019

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Post-disaster damage mapping is an essential task following tragic events such as hurricanes, earthquakes, and tsunamis. It is also a time-consuming and risky task that still often requires the sending of experts on the ground to meticulously map and assess the damages. Presently, the increasing number of remote-sensing satellites taking pictures of Earth on a regular basis with programs such as Sentinel, ASTER, or Landsat makes it easy to acquire almost in real time images from areas struck by a disaster before and after it hits. While the manual study of such images is also a tedious task, progress in artificial intelligence and in particular deep-learning techniques makes it possible to analyze such images to quickly detect areas that have been flooded or destroyed. From there, it is possible to evaluate both the extent and the severity of the damages. In this paper, we present a state-of-the-art deep-learning approach for change detection applied to satellite images taken before and after the Tohoku tsunami of 2011. We compare our approach with other machine-learning methods and show that our approach is superior to existing techniques due to its unsupervised nature, good performance, and relative speed of analysis.

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Section: Supervised Methods For Change Detection and Damage Mappingmentioning

confidence: 99%

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

2019

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“…We expand upon such studies by exploring the application of LiDAR, a variety of predictor variables and RF machine learning over a large spatial extent, which is uncommon in the literature. Although this study focuses on probabilistic mapping using the RF traditional machine learning method, is should be noted that deep learning methods that rely on convolutional neural networks have been explored for slope failure mapping and predictive tasks in several recent studies [25,29,30,[56][57][58][59][60][61].…”

Section: Mapping Slope Failures and Susceptibilitymentioning

confidence: 99%

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Maxwell

Sharma²,

Kite

et al. 2020

Remote Sensing

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The probabilistic mapping of landslide occurrence at a high spatial resolution and over a large geographic extent is explored using random forests (RF) machine learning; light detection and ranging (LiDAR)-derived terrain variables; additional variables relating to lithology, soils, distance to roads and streams and cost distance to roads and streams; and training data interpreted from high spatial resolution LiDAR-derivatives. Using a large training set and all predictor variables, an area under the receiver operating characteristic (ROC) curve (AUC) of 0.946 is obtained. Our findings highlight the value of a large training dataset, the incorporation of a variety of terrain variables and the use of variable window sizes to characterize the landscape at different spatial scales. We also document important variables for mapping slope failures. Our results suggest that feature selection is not required to improve the RF modeling results and that incorporating multiple models using different pseudo absence samples is not necessary. From our findings and based on a review of prior studies, we make recommendations for high spatial resolution, large-area slope failure probabilistic mapping.

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“…Convolutional neural networks (CNN) have shown their success in image processing including change detection [33][34][35][36][37][38]. In [35][36][37], researchers design various fully convolutional networks to perform end-to-end detection. In [33,34], recurrent neural networks are combined with CNN to extract features from multitemporal images.…”

Section: Change Detectionmentioning

confidence: 99%

Deep Siamese Networks Based Change Detection with Remote Sensing Images

et al. 2021

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Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.

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End-to-end Change Detection Using a Symmetric Fully Convolutional Network for Landslide Mapping

Cited by 31 publications

References 18 publications

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Automatic Post-Disaster Damage Mapping Using Deep-Learning Techniques for Change Detection: Case Study of the Tohoku Tsunami

Slope Failure Prediction Using Random Forest Machine Learning and LiDAR in an Eroded Folded Mountain Belt

Deep Siamese Networks Based Change Detection with Remote Sensing Images

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