Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data

• URL: http://arxiv.org/abs/2005.03221v2
• Date: Wed, 13 May 2020 03:20:00 GMT
• Title: Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
• Authors: Nantheera Anantrasirichai, Juliet Biggs, Krisztina Kelevitz, Zahra Sadeghi, Tim Wright, James Thompson, Alin Achim, David Bull
• Abstract summary: We adapt a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. We focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.
• Score: 7.503635457124339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The large volumes of Sentinel-1 data produced over Europe are being used to develop pan-national ground motion services. However, simple analysis techniques like thresholding cannot detect and classify complex deformation signals reliably making providing usable information to a broad range of non-expert stakeholders a challenge. Here we explore the applicability of deep learning approaches by adapting a pre-trained convolutional neural network (CNN) to detect deformation in a national-scale velocity field. For our proof-of-concept, we focus on the UK where previously identified deformation is associated with coal-mining, ground water withdrawal, landslides and tunnelling. The sparsity of measurement points and the presence of spike noise make this a challenging application for deep learning networks, which involve calculations of the spatial convolution between images. Moreover, insufficient ground truth data exists to construct a balanced training data set, and the deformation signals are slower and more localised than in previous applications. We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of real UK velocity map, and iii) enhanced over-wrapping techniques. Using velocity maps spanning 2015-2019, our framework detects several areas of coal mining subsidence, uplift due to dewatering, slate quarries, landslides and tunnel engineering works. The results demonstrate the potential applicability of the proposed framework to the development of automated ground motion analysis systems.

Related papers

• DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal Forecasting [24.00162014044092]
  Earth science systems rely heavily on the extensive deployment of sensors. Traditional approaches to sensor deployment utilize specific algorithms to design and deploy sensors. In this paper, we introduce for the first time the concept of dynamic sparse training and are committed to adaptively, dynamically filtering important sensor data.
  arXiv  Detail & Related papers  (2024-03-05T12:31:24Z)
• Implicit neural representation for change detection [15.741202788959075]
  Most commonly used approaches to detecting changes in point clouds are based on supervised methods. We propose an unsupervised approach that comprises two components: Implicit Neural Representation (INR) for continuous shape reconstruction and a Gaussian Mixture Model for categorising changes. We apply our method to a benchmark dataset comprising simulated LiDAR point clouds for urban sprawling.
  arXiv  Detail & Related papers  (2023-07-28T09:26:00Z)
• Classification of structural building damage grades from multi-temporal photogrammetric point clouds using a machine learning model trained on virtual laser scanning data [58.720142291102135]
  We present a novel approach to automatically assess multi-class building damage from real-world point clouds. We use a machine learning model trained on virtual laser scanning (VLS) data. The model yields high multi-target classification accuracies (overall accuracy: 92.0% - 95.1%)
  arXiv  Detail & Related papers  (2023-02-24T12:04:46Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World [83.36195426897768]
  We propose VRVO, a novel framework for retrieving the absolute scale from virtual data. We first train a scale-aware disparity network using both monocular real images and stereo virtual data. The resulting scale-consistent disparities are then integrated with a direct VO system.
  arXiv  Detail & Related papers  (2022-03-11T01:51:54Z)
• Context-Preserving Instance-Level Augmentation and Deformable Convolution Networks for SAR Ship Detection [50.53262868498824]
  Shape deformation of targets in SAR image due to random orientation and partial information loss is an essential challenge in SAR ship detection. We propose a data augmentation method to train a deep network that is robust to partial information loss within the targets.
  arXiv  Detail & Related papers  (2022-02-14T07:01:01Z)
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach [93.67588414950656]
  In this paper, we investigate an unmanned aerial vehicle (UAV)-assisted Internet-of-Things (IoT) system in a 3D environment. We present a TD3-based trajectory design for completion time minimization (TD3-TDCTM) algorithm. Our simulation results show the superiority of the proposed TD3-TDCTM algorithm over three conventional non-learning based baseline methods.
  arXiv  Detail & Related papers  (2021-07-23T03:33:29Z)
• SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps [0.17188280334580192]
  We propose a neural network based data inversion approach to reduce structured contamination sources. We focus on the mapmaking for Planck High Frequency Instrument (Planck-HFI) data and the removal of large-scale systematic effects.
  arXiv  Detail & Related papers  (2020-12-17T16:13:56Z)
• Improving Monocular Depth Estimation by Leveraging Structural Awareness and Complementary Datasets [21.703238902823937]
  We propose a structure-aware neural network with spatial attention blocks to exploit the spatial relationship of visual features. Second, we introduce a global focal relative loss for uniform point pairs to enhance spatial constraint in the prediction. Third, based on analysis of failure cases for prior methods, we collect a new Hard Case (HC) Depth dataset of challenging scenes.
  arXiv  Detail & Related papers  (2020-07-22T08:21:02Z)
• Weakly-supervised land classification for coastal zone based on deep convolutional neural networks by incorporating dual-polarimetric characteristics into training dataset [1.125851164829582]
  We explore the performance of DCNNs on semantic segmentation using spaceborne polarimetric synthetic aperture radar (PolSAR) datasets. The semantic segmentation task using PolSAR data can be categorized as weakly supervised learning when the characteristics of SAR data and data annotating procedures are factored in. Three DCNN models, including SegNet, U-Net, and LinkNet, are implemented next.
  arXiv  Detail & Related papers  (2020-03-30T17:32:49Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.