Multivariate time series classification (MTSC) has broad applications in numerous domains. Existing MTSC methods typically focus on either temporal dynamics or variable interactions of the data, often overlooking cross-scale couplings among different variables. To bridge this gap, we propose Scale-Variable Graph Learning (SVGL), a novel framework that effectively captures data-inherent scale-variable interactions for MTSC. SVGL begins with spectral analysis to adaptively identify key periodic scales for each variable. A period-aware reservoir computing network is then incorporated to fit the variable at these scales, encoding the sequential and periodic dynamics into multi-scale dynamic representations. Subsequently, we construct a scale-variable graph to model interactions of the encoded temporal dynamics, where nodes represent scale-variable pairs and edges denote their correlations. After sparsely initializing the graph via nearest neighbors, a parallel graph learning architecture is integrated in SVGL, combining global graph convolutional and sample-specific graph attention to aggregate effective features for classification. Extensive experiments on 30 UEA datasets demonstrate that SVGL outperforms state-of-the-art baselines in accuracy and maintains low training overhead.

Zero-shot classifier expansion aims to adapt existing model to new, unseen classes. It utilizes class attributes or textual descriptions to learn a mapping from the semantic space to the classifier's weight space, without requiring new visual training data. However, the learning process for this mapping relies solely on correlating semantic patterns with their corresponding classifier weights and lacks explicit modeling of inter-class differences. This makes it difficult for the model to capture the critical discriminative features required to define classification boundaries. To overcome this limitation, we reframe the problem from a causal perspective and introduce a novel framework driven by counterfactuals. Our method first generates factual descriptions alongside corresponding inter-class counterfactuals to pinpoint the causal attributes essential for classification, then refines these representations via a mutual purification process, and finally leverages a novel separation loss to explicitly push the factual and counterfactual classifier weights apart. This strategy forces the model to forge clearer and more discriminative classification boundaries, achieving more accurate and robust classification. Extensive experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods.

Fault Diagnosis (FD) on sequential data suffers from irregular sampling (with missing values), limited training data, and varying underlying environments. In response, this paper proposes FD by adjoint learning in continuous-time model space. Model-Space Learning employs well-fitted models that capture data's dynamics (i.e., changing information) as more stable and concise representations of the original data. The Continuous-Time Reservoir Computing Network (CT-Res) is first introduced, which embeds Ordinary Differential Equation (ODE) within the reservoir-based hidden layer to govern continuous-time hidden-state evolution, naturally handling irregular sampling without relying on fixed time steps and effectively capturing intrinsic data dynamics. By fitting each sequence via CT-Res and representing it with the fitted model, the original sequences are mapped from the data space into the continuous-time model space. We further develop an adjoint learning strategy by incorporating a discrete-time "adjoint Echo State Network (ESN)" that shares structure and parameters with CT-Res, thus enabling efficient training by bypassing the computationally intensive ODE solver, with joint optimization of fitting accuracy and class discrimination in the model space. Experiments on multiple FD benchmarks highlight the effectiveness and efficiency of our study, particularly with missing values and scarce training data.

Precise delineation of tumor target volumes in radiation therapy is a knowledge-intensive clinical task governed by three formally characterizable knowledge types: declarative geometric priors encoding anatomically plausible contour constraints, structural relational knowledge capturing rotational symmetry in polygon vertex sequences, and procedural tolerance knowledge defining clinically acceptable boundary deviations. Existing pixel-level segmentation methods discard this knowledge entirely, producing topologically inconsistent masks that require costly manual correction. We propose a knowledge-guided framework that explicitly encodes all three knowledge forms into both model architecture and training supervision. Specifically, (1) a Vision Transformer encoder captures global spatial knowledge via self-attention, transcending the locality limitations of convolutional kernels; (2) Rotary Positional Encoding embeds the rotational symmetry knowledge inherent in polygon vertex sequences, capturing a structural prior that standard absolute encodings fundamentally cannot represent; and (3) the proposed Convolutional Smoothing Loss operationalizes clinical positional tolerance knowledge by assigning spatially graded supervision signals, distinguishing catastrophic boundary violations from clinically acceptable minor deviations. Evaluated on the MSD Pancreas Tumour dataset and a proprietary cervical cancer CT dataset, the proposed model consistently outperforms CNN-based and Transformer-based baselines across all metrics. Ablation studies and robustness analysis under Gaussian noise confirm the independent contribution of each knowledge-encoding component.

Efficient anomaly detection of irregular sequences, especially those characterized by non-uniform sampling from discontinuous operations or unreliable sensors, presents challenges across various fields. In response, this paper introduces irregular-sequence classification in ''Ct-Echo Model Space''. A novel Continuous-time Echo Network (Ct-Echo) is proposed to fit irregular sequences, efficiently capturing their inherent dynamic characteristics. Ct-Echo utilizes the ''Echo'' mechanism, where history information influences the current state and diminishes over time, and employs Ordinary Differential Equation (ODE) to construct continuous-time transition of hidden states. Each sequence is individually fitted via Ct-Echo to derive a readout model. These fitted models, capturing the dynamic characteristics of the original data, serve as representations of the corresponding sequences, thus mapping the original data from the data space to the Ct-Echo model space. Anomaly detection is further performed in this model space, evaluating differences between models rather than directly on the original sequences. Our method enhances real-time processing and lessens reliance on the amount of labeled training data, as demonstrated by experimental studies.

Anomaly detection in sequential signals is gaining prominence, especially with limited training data and timeliness requirements. Fully extracting the data-inside changing information, we propose a novel Wavelet-Enhanced Reservoir Computing framework (WE-Res). Our framework uses Discrete Wavelet Transform (DWT) to decompose signals for multi-level detail and trend extraction recursively. Each level is fitted using an Echo State Network (ESN) respectively, extracting its inside dynamic features into fitted models. Integrating these dynamic features creates a "multi-level dynamic feature" that enhances signal representation, aiding in distinguishing between normal and anomalous patterns. We further introduce a dual-objective optimization to refine ESNs’ reservoirs, increasing the fitting accuracy and improving category discrimination among the captured features. Validation on real-world data confirms our method’s effectiveness, especially in data-limited scenarios and less training time compared to recent baselines.

Fault Diagnosis (FD) in time-varying systems faces challenges like limited training data, varying environments, and timeliness. Building upon the framework of model-space learning (MSL), we introduce co-objective learning in Dynamic-Integration network (DynInt) model space as a solution for FD. MSL involves utilizing well-fitted models that capture the dynamics within the data as more stable and parsimonious representations of the original data. DynInt integrates pooling and reservoir computing to adequately capture the data-inherent multi-scale dynamics. Representing the signal with the fitted DynInt model transforms the original signal from data space into the DynInt model space. A co-objective optimization is further introduced on DynInt, improving the fitting accuracy and category discrimination. Validation on real-world data confirms our method’s effectiveness, especially in data-limited scenarios.

A key challenge in personalized healthcare is identifying optimal intervention sequences to guide temporal systems toward target outcomes, a novel problem we formalize as counterfactual target achievement. In addressing this problem, directly adopting counterfactual estimation methods face compounding errors due to the unobservability of counterfactuals. To overcome this, we propose Variational Counterfactual Intervention Planning (VCIP), which reformulates the problem by modeling the conditional likelihood of achieving target outcomes, implemented through variational inference. By leveraging the g-formula to bridge the gap between interventional and observational log-likelihoods, VCIP enables reliable training from observational data. Experiments on both synthetic and real-world datasets show that VCIP significantly outperforms existing methods in target achievement accuracy.

Analyzing inherent temporal dynamics is a critical pathway for time series classification, where Reservoir Computing (RC) exhibits effectiveness and high efficiency. However, typical RC considers recursive updates from adjacent states, struggling with long-term dependencies. In response, this paper proposes a Spectral-Aware Reservoir Computing framework (SARC), incorporating spectral insights to enhance long-term dependency modeling. Prominent frequencies are initially extracted to reveal explicit or implicit cyclical patterns. For each prominent frequency, SARC further integrates a Frequency-informed Reservoir Network (FreqRes) to adequately capture both sequential and cyclical dynamics, thereby deriving effective dynamic features. Synthesizing these features across various frequencies, SARC offers a multi-scale analysis of temporal dynamics and improves the modeling of long-term dependencies. Experiments on public datasets demonstrate that SARC achieves state-of-the-art results, while maintaining high efficiency compared to existing methods.

Zero-shot learning has shown significant potential for creating cost-effective and flexible systems to expand classifiers to new categories. However, existing methods still rely on manually created attributes designed by domain experts. Motivated by the widespread success of large language models (LLMs), we introduce an LLM-driven framework for class-incremental learning that removes the need for human intervention, termed Classifier Expansion with Multi-vIew LLM knowledge (CEMIL). In CEMIL, an LLM agent autonomously generates detailed textual multi-view descriptions for unseen classes, offering richer and more flexible class representations than traditional expert-constructed vectorized attributes. These LLM-derived textual descriptions are integrated through a contextual filtering attention mechanism to produce discriminative class embeddings. Subsequently, a weight injection module maps the class embeddings to classifier weights, enabling seamless expansion to new classes. Experimental results show that CEMIL outperforms existing methods using expert-constructed attributes, demonstrating its effectiveness for fully automated classifier expansion without human participation.

Ground Penetrating Radar (GPR) provides detailed subterranean insights. Nevertheless, underground diagnosis via GPR is hindered by the fact that training data typically contain only normal samples, along with the complexity of GPR data’s wave-collection characteristics. This paper proposes subsurface anomaly detection within the Cubic Correlation Reservoir Network (CuCoRes) model space. CuCoRes incorporates three reservoirs with spatial correlation adjustment in each direction to adequately and accurately capture multi-directional dynamics (i.e., changing information) within GPR data. Fitting GPR data with CuCoRes and representing data with fitted models, the original GPR data is mapped into a category-discriminative CuCoRes model space, where anomalies could be efficiently identified and categorized based on model dissimilarities. Our approach leverages only limited normal GPR data, easily accessible, to support subsequent anomaly detection and categorization, enhancing its applicability in practical scenarios. Experiments on real-world data demonstrate its effectiveness, outperforming state-of-the-art.

Fault Diagnosis (FD) in time-varying data presents considerations such as limited training data, intra- and inter-dimensional correlations, and constraints of training time. In response, this paper introduces FD in the Reservoir-Embedded-Directional Network (REDNet) model space. Model-oriented methods utilize well-fitted networks or functions, denoted as "models" that capture data's changing information, as more stable and parsimonious representations of the data. Our approach employs REDNet for data fitting, wherein multiple reservoirs are organized along intrinsic correlation directions to establish intra- and inter-dimensional dependencies, thereby capturing multi-directional dynamics in high-dimensional data. Representing each data instance with an independently fitted REDNet model maps these instances into a class-separable REDNet model space, where FD could be performed on the models rather than the original data. Concentrating on the data-intrinsic dynamics, our method achieves rapid training speeds, and maintains robust performance even with minimal training data. Experiments on several datasets demonstrate its effectiveness.

Learning the causality between variables, known as DAG structure learning, is critical yet challenging due to issues such as insufficient data and noise. While prior knowledge can improve the learning process and refine the DAG structure, incorporating prior knowledge is not without pitfalls. In particular, we find that the gap between the imprecise prior knowledge and the exact weights modeled by existing methods may result in deviation in edge weights. Such deviation can subsequently cause significant inaccuracies when learning the DAG structure. This paper addresses this challenge by providing a theoretical analysis of the impact of deviation in edge weights during the optimization process of structure learning. We identify two special graph patterns that arise due to the deviation and show that their occurrence increases as the degree of deviation grows. Building on this analysis, we propose the Pattern-Guided Adaptive Prior (PGAP) framework. PGAP detects these patterns as structural signals during optimization and adaptively adjusts the structure learning process to counteract the identified weight deviation, thereby improving the integration of prior knowledge. Experiments verify the effectiveness and robustness of the proposed method.

Anomaly detection (AD) is gaining prominence, especially in situations with limited labeled data or unknown anomalies, demanding an efficient approach with minimal reliance on labeled data or prior knowledge. Building upon the framework of Learning in the Model Space (LMS), this paper proposes conducting AD through Learning in the Multi-Level Model Spaces (MLMS). LMS transforms the data from the data space to the model space by representing each data instance with a fitted model. In MLMS, to fully capture the dynamic characteristics within the data, multi-level details of the original data instance are decomposed. These details are individually fitted, resulting in a set of fitted models that capture the multi-level dynamic characteristics of the original instance. Representing each data instance with a set of fitted models, rather than a single one, transforms it from the data space into the multi-level model spaces. The pairwise difference measurement between model sets is introduced, fully considering the distance between fitted models and the intra-class aggregation of similar models at each level of detail. Subsequently, effective AD can be implemented in the multi-level model spaces, with or without sufficient multi-class labeled data. Experiments on multiple AD datasets demonstrate the effectiveness of the proposed method.

Time series classification (TSC) is crucial in many applications, yet accurately modeling complex time series patterns remains challenging. Model-based TSC strives to aptly model time series by capturing their intrinsic temporal dynamics, deriving effective dynamic representations for classification. Despite significant progress in this domain, existing works are still constrained by a singular and overly simplistic modeling paradigm, which proves inadequate to handle the multiscale hierarchies inherent in time series. Additionally, the prevailing reliance on manual model configuration fails to address the diverse dynamic characteristics across varying data scenarios. In this paper, we amalgamate multiple recurrent reservoirs to devise a model-based Multiscale Temporal Dynamic Learning (MsDL) approach. These reservoirs are endowed with varied recurrent connection skips, ensuring a comprehensive capture of temporal dynamics across different timescales. We also present a multi-objective optimization algorithm, which adaptively configures the memory length of each reservoir, allowing for more accurate time series modeling. This optimization further encourages time series from the same class to look closer, while separating those from different classes, thereby enhancing the category-discriminability. Extensive experiments on public datasets demonstrate that MsDL outperforms the state-of-the-art methods. Additionally, ablation studies confirm that our multiscale design and optimization algorithm effectively enhance classification accuracy.

The spectral shape holds crucial information for Audio Classification (AC), encompassing the spectrum's envelope, details, and dynamic changes over time. Conventional methods utilize cepstral coefficients for spectral shape description but overlook its variation details. Deep-learning approaches capture some dynamics but demand substantial training or fine-tuning resources. The Learning in the Model Space (LMS) framework precisely captures the dynamic information of temporal data by utilizing model fitting, even when computational resources and data are limited. However, applying LMS to audio faces challenges: 1) The high sampling rate of audio hinders efficient data fitting and capturing of dynamic information. 2) The Dynamic Information of Partial Spectral Shapes (DIPSS) may enhance classification, as only specific spectral shapes are relevant for AC. This paper extends an AC framework called Effective Dynamic Information Capture (EDIC) to tackle the above issues. EDIC constructs Mel-Frequency Cepstral Coefficients (MFCC) sequences within different dimensional intervals as the fitted data, which not only reduces the number of sequence sampling points but can also describe the change of the spectral shape in different parts over time. EDIC enables us to implement a topology-based selection algorithm in the model space, selecting effective DIPSS for the current AC task. The performance on three tasks confirms the effectiveness of EDIC.

With the rapid growth of audio data, there's a pressing need for automatic audio classification. As a type of time-series data, audio exhibits waveform fluctuations in both the time and frequency domains that evolve over time, with similar instances sharing consistent patterns. This study introduces the Audio Scanning Network (ASNet), designed to leverage abundant information for achieving stable and effective audio classification. ASNet captures real-time changes in audio waveforms across both time and frequency domains through reservoir computing, supported by Reservoir Kernel Canonical Correlation Analysis (RKCCA) to explore correlations between time-domain and frequency-domain waveform fluctuations. This innovative approach empowers ASNet to comprehensively capture the changes and inherent correlations within the audio waveform, and without the need for time-consuming iterative training. Instead of converting audio into spectrograms, ASNet directly utilizes audio feature sequences to uncover associations between time and frequency fluctuations. Experiments on environmental sound and music genre classification tasks demonstrate ASNet's comparable performance to state-of-the-art methods.

Change detection (CD) involves comparing multitemporal images captured at different times. However, the existing CD methods mainly focus on registered images, neglecting the challenges posed by unregistered image pairs. Besides, the lack of explicit training in distinguishing changes from unchanged areas results in noisy outcomes. To this end, we propose to leverage the convolutional neural network (CNN) and generative adversarial network (GAN) to automatically extract the common region from unregistered images and conduct CD in the extracted region. Specifically, our method utilizes CNN to extract high-level convolutional information from unregistered images. Subsequently, feature matching is employed to identify common regions between the two images. These extracted regions serve as input for the supporting expansion strategy, enabling the creation of a training set for the subsequent training of GAN, which consists of a generator incorporating the channel attention module (CAM) and a discriminator. The optimized generator then produces multiple enhanced coregistered images, finally compared to generate the change map. The experimental results show that for the changed scene image pairs, the proposed method is 4.46% higher than the best performance of the comparison methods on overall accuracy (OA), 2.65% higher on precision (Pre), and 2.3% higher on$F1$score (F1). For the unchanged scene image pairs, the OA is improved by 2.72%. These demonstrate the superior performance of our proposed method in CD, especially on unregistered image pairs.

Ground Penetrating Radar (GPR) has been widely used in pipeline detection and underground diagnosis. In practical applications, the characteristics of the GPR data of the detected area and the likely underground anomalous structures could be rarely acknowledged before fully analyzing the obtained GPR data, causing challenges to identify the underground structures or anomalies automatically. In this article, a GPR B-scan image diagnosis method based on learning in the model space is proposed. The idea of learning in the model space is to use models fitted on parts of data as more stable and parsimonious representations of the data. For the GPR image, 2-Direction Echo State Network (2D-ESN) is proposed to fit the image segments through the next item prediction. By building the connections between the points on the image in both the horizontal and vertical directions, the 2D-ESN regards the GPR image segment as a whole and could effectively capture the dynamic characteristics of the GPR image. And then, semi-supervised and supervised learning methods could be further implemented on the 2D-ESN models for underground diagnosis. Experiments on real-world datasets are conducted, and the results demonstrate the effectiveness of the proposed model.

Urban development requires meticulous attention to subsurface conditions to ensure the reliable operation of roads and facilities. Ground Penetrating Radar (GPR) offers a non-destructive solution for subsurface anomaly detection. However, the invisible and variable subsurface environments, combined with limited labeled data, make the detection process challenging. While the “Learning in the Model Space (LMS)” shows efficacy by fitting the data to capture the inherent dynamics and representing the original data with fitted models for further process, it falls short in handling GPR B-scan data due to its unidirectional fitting, static model metric, and manual model adjustment. Addressing these challenges, this paper introduces learning in the Dual-Directional Dynamic-captured (D3) model space. We frame the collected GPR B-scan data and fit the GPR data in each frame both horizontally and vertically, encapsulating the dual-directional dynamics within this data frame into a concise D3 model. This D3 model then serves as a representation for this GPR data, mapping the original data from the data space to the D3 model space, and enabling learning on the models rather than the raw data. With the proposed parameterized model metric, our method offers adaptability to diverse data scenarios. We further introduce an optimization algorithm that fine-tunes the fitting process and establishes an optimal model metric, resulting in a “category-distinctive” D3 model space. This enables precise anomaly detection and classification within the D3 model space. Experiments on GPR data underline the superiority of our method in real-world applications.

Currently, numerous deep learning (DL) methods have been proposed for the recognition of ground penetrating radar (GPR) B-scan images. Due to the sensitivity of GPR imaging to local underground conditions, DL models trained in other underground environment areas are likely to fail in new areas. Consequently, organizing and labeling new GPR images to train models have become a widely adopted approach in practical applications. However, expert annotation is costly, making it often difficult to collect large-scale datasets with high-quality annotations. Some studies have attempted to improve the quality of image annotations by integrating the efforts of multiple annotators. This process can be constrained by the varying levels of expertise and attention spans of the annotators, which may lead to the presence of errors or contradictions in the provided labels. To address these challenges, this article proposes a method for underground target recognition aimed at multisource annotation tasks. A probability multisource label aggregation (PMLA) module is designed to estimate the reliability of multisource labels, and a label-sensitive regularization (LSR) module is introduced to mitigate the negative impact of potentially erroneous labels on model training. Extensive experiments are conducted on multiple GPR B-scan datasets. The experimental results demonstrate the advantages of the proposed method in handling annotation conflicts and improving the accuracy of underground target recognition.

Detecting and classifying subsurface anomalies in urban infrastructure management is crucial and challenging due to the complexity of underground conditions, with ground penetrating radar (GPR) providing essential noninvasive insights. Challenges in GPR data analysis include: acquiring accurately labeled datasets that cover various anomaly categories, and achieving precise localization of these underground anomalies from GPR data. Addressing these limitations, this article introduces the spatial-dynamic-combined (SpaDyn) framework to identify and classify anomalies in GPR B-scan data, which consists of two main components: Anomaly Region Spatial Localization and Dynamic Feature Extraction. Using both normal and unclassified anomalous data, we first construct an abnormal region extractor extended from the Faster-RCNN architecture to determine the spatial coordinates of potential anomalies. Each identified region is then fit using the bidirectional reservoir computing network (BiD-Res), designed with two reservoirs to capture dynamic features in both horizontal and vertical directions, essential for GPR data due to the continuity of subterranean media and electromagnetic waves. The fit readout model from BiD-Res serves as the dynamic feature representation of the original region, thus transforming the identified regions into a size-independent and category-discriminative “dynamic feature space.” These dynamic features are then clustered to help ascertain the specific type of each region. Experimental results on real-world datasets validated the effectiveness of SpaDyn, particularly in scenarios where specific information about anomaly categories is unavailable.

As data sources become ever more numerous with increased feature dimensionality, feature selection for multiview data has become an important technique in machine learning. Semi-supervised multiview feature selection (SMFS) focuses on the problem of how to obtain a discriminative feature subset from heterogeneous feature spaces in the case of abundant unlabeled data with little labeled data. Most existing methods suffer from unreliable similarity graph structure across different views since they separate the graph construction from feature selection and use the fixed graphs that are susceptible to noisy features. Furthermore, they directly concatenate multiple feature projections for feature selection, neglecting the contribution diversity among projections. To alleviate these problems, we present an SMFS to simultaneously select informative features and learn a unified graph through the data fusion from aspects of feature projection and similarity graph. Specifically, SMFS adaptively weights different feature projections and flexibly fuses them to form a joint weighted projection, preserving the complementarity and consensus of the original views. Moreover, an implicit graph fusion is devised to dynamically learn a compatible graph across views according to the similarity structure in the learned projection subspace, where the undesirable effects of noisy features are largely alleviated. A convergent method is derived to iteratively optimize SMFS. Experiments on various datasets validate the effectiveness and superiority of SMFS over state-of-the-art methods.

The remote sensing (RS) field has an increasing research interest in using deep learning (DL) models to recognize kinds of RS data, leading to a great demand for training data annotation. Due to the high cost of expertise, using nonexperts to label data has become an important way to improve labeling efficiency. Commonly, a single data sample is labeled by multiple annotators and the most voted label is accepted to promise accuracy. But in the RS context, the widely admitted strategy could lose effect. Usually RS data involve considerable classes on account of the complexity of surface environments, which is prone to interclass similarity difficult to distinguish. Annotators without expertise probably make mistakes on these indistinguishable classes, thus causing error voted labels. Although classification of different characteristics in RS data has been widely documented, the nonexpert annotators are unfamiliar with these expertise, and it is difficult to force them to handle specialized labeling skills. To address the issues, this article bases multiannotator label selection on the investigation of annotators’ own ability in distinguishing similar classes of images. A quality evaluation process is designed which weights the labels from capable annotators higher than those from weak ones. By a multi-round quality evaluation algorithm, correct labels could outcompete the wrong ones even disadvantaged in numbers. Experimental results demonstrate the advance of the proposed method on the RS datasets.

Maintaining and upgrading underground pipelines are major undertakings in urban operations, where accurately locating buried pipelines has long been an issue. In this article, we propose a pipeline mapping method based on integrating multipositional pipeline data, which includes the multisensor data acquisition (MDA) platform and the scalable probability-based pipeline mapping (SP-PM) model. To effectively collect pipeline data at multiple positions, several pipeline detecting and positioning sensors are equipped in the MDA platform. Different types of sensor data are synchronously collected and processed to obtain manageable pipeline data at multiple positions within the detected area, such as the radius, depth, and positioned points of underground pipelines. The SP-PM model is then proposed, where the obtained pipeline data are probabilistically described and classified into classes. Each class contains the pipeline data possibly generated by the same pipeline. The classified data are then iteratively integrated to estimate the pipeline map with the maximum probability. The SP-PM model probabilistically estimates the degree of correlation between the multipositional pipeline data and each potential pipeline, and it has no strict requirement on existing statutory records or limits on the number of detections per pipeline. Unrecorded pipelines could be identified and involved into the generated pipeline map, along with continuous adjusting of the pipelines’ number. We conducted experiments on real-world environments. The experimental results verify the accuracy and efficiency of the proposed method for buried pipeline mapping.

Ground Penetrating Radar (GPR) provides an effective means for underground anomaly detection, but is also accompanied by some practical issues such as the lack of prior knowledge, insufficient labeled data, and timeliness constraints. In this paper, we propose detecting underground anomalies by adequately capturing multi-directional changing information within GPR B-scan data, which extends the framework of Model-Space Learning (MSL). MSL aims to transform the data from the data space to the model space by representing the original data with fitted models that capture the changes within the data. In GPR data, due to the continuity of the underground medium and electromagnetic wave, there is not only effective changing information in each column of data along the vertical direction, but also in the horizontal detecting direction according to the subsurface environments and existing underground structures. To fully capture the changing information within GPR data, we fit the GPR data along multiple directions respectively, and synthesize the fitted models into a Comprehensive-Change-Captured model (C3 model) wherein multi-directional changing information within the original data is captured. Representing the original data with the C3 model transforms the data from the data space to the C3 model space. The distance metric between C3 models is then introduced, and learning methods could be efficiently implemented on the models. Experiments are conducted on GPR data collected along urban roads, with or without prior knowledge about the existing subsurface anomalies. The obtained results demonstrate the effectiveness of the proposed method.

Incomplete instances with various missing values in real-world scenes have brought challenges to the classification tasks. Many existing methods impute the incomplete instances based on their neighbouring instances before classification. However, the construction of such neighbourhood relationships in the original data space may become unreliable since missing values could disturb the traditional distance measurement. Moreover, these methods decouple the imputation from the classification, which makes it difficult for the former to learn from the supervised information, resulting in sub-optimal performance. To this end, this paper proposes graph neural networks for missing values classification (GNN4MV), which directly classify the incomplete instances based on their neighbourhood relationships constructed in a novel task-driven metric space. Specifically, the supervised information is taken as additional guidance in a task-driven metric space to reduce the impact of the missing values for neighbourhood relationship construction. Furthermore, a novel neighbourhood graph convolutional network is proposed in GNN4MV, which enables direct classification of the incomplete instances without imputation by utilizing the graph topology in the constructed neighbourhood relationships. Experiments on real-world datasets demonstrate the robustness and effectiveness of the proposed algorithm.

There is a need to map underground pipelines due to non-available existing pipeline maps caused by poor management of statutory records and insufficient updating of documentation whenever pipeline construction or rerouting occurs. By fusing multi-source data, a novel method to map underground pipelines is proposed in this paper. Statutory records of the underground pipelines are converted to the initial pipeline map. Pipeline information obtained from manhole covers and remote sensing technologies are normalized into the pipeline data set composed of detected points. The Probabilistic Pipeline Mapping Model (PPMM) is then proposed to map the buried pipelines from the conducted pipeline data set, with or without statutory pipeline records. In this model, each detected point is classified into the specific pipeline that most likely generates the data of this point, and detected points generated from the same pipeline are fitted to revise the pipelines’ locations and directions. The above classification and fitting operations are performed iteratively, and PPMM would output the pipeline map with the highest probability. Experimental studies on real-world datasets are conducted and analyzed, and the obtained results demonstrate the effectiveness of the proposed method.

With the rapid expansion of urban areas and the increasing use of electricity, the need for locating buried cables is becoming urgent. In this paper, a novel method to locate underground cables based on Ground Penetrating Radar (GPR) and Gaussian-process regression is proposed. Firstly, the coordinate system of the detected area is conducted, and the input and output of locating buried cables are determined. The GPR is moved along the established parallel detection lines, and the hyperbolic signatures generated by buried cables are identified and fitted, thus the positions and depths of some points on the cable could be derived. On the basis of the established coordinate system and the derived points on the cable, the clustering method and cable fitting algorithm based on Gaussian-process regression are proposed to find the most likely locations of the underground cables. Furthermore, the confidence intervals of the cables’ locations are also obtained. Both the position and depth noises are taken into account in our method, ensuring the robustness in different environments and equipment. Experiments on real-world datasets are conducted, and the obtained results demonstrate the effectiveness of the proposed method.

Detecting changed regions in paired satellite images plays a key role in many remote sensing applications. The evolution of recent techniques could provide satellite images with very high spatial resolution (VHR) but made it challenging to apply image coregistration, and many change detection methods are dependent on its accuracy.Two images of the same scene taken at different time or from different angle would introduce unregistered objects and the existence of both unregistered areas and actual changed areas would lower the performance of many change detection algorithms in unsupervised condition.To alleviate the effect of unregistered objects in the paired images, we propose a novel change detection framework utilizing a special neural network architecture -- Generative Adversarial Network (GAN) to generate many better coregistered images. In this paper, we show that GAN model can be trained upon a pair of images through using the proposed expanding strategy to create a training set and optimizing designed objective functions. The optimized GAN model would produce better coregistered images where changes can be easily spotted and then the change map can be presented through a comparison strategy using these generated images explicitly.Compared to other deep learning-based methods, our method is less sensitive to the problem of unregistered images and makes most of the deep learning structure.Experimental results on synthetic images and real data with many different scenes could demonstrate the effectiveness of the proposed approach.

The demand for mapping the buried cables is increasing dramatically with the rapid expansion of urban area, but there are few specific procedures and approaches to map underground cables without disturbing the normal electricity supply. In this letter, a cable-mapping approach based on the ground-penetrating radar (GPR) is proposed, which consists of two parts. First, the parallel scan lines are established along which the GPR is moved to obtain the B-scan images; the hyperbolic shapes on these obtained images are identified and fitted; and the locations and depths of the detected sample points of the buried cables could be derived from these hyperbolas. Then, due to different terrain and surrounding obstacles, as well as the use of pipe-jacking technology,1the underground cables may not be straight. The detected points are interpolated by a three-dimensional spline interpolation algorithm to obtain a smooth 3-D curve with location and depth information. Compared with the 2-D model, the 3-D curve could visualize the direction of buried cables more intuitively. Also, in comparison with other interpolation algorithms, the error caused by the proposed interpolation is minimized on the chosen data sets. Experiments on real-world data sets are conducted, and the obtained results demonstrate the effectiveness of the proposed model.1Pipe-jacking is a trenchless technology for buried utilities [1].

In this paper, an efficient plastic pipe detecting model is proposed, which combines the ground penetrating radar (GPR) and the electric field method. The model consists of the electric field locating model (EFLM) and the GPR B-scan image interpreting (GBII) model. Synchronized electric field and GPR data are collected through a data acquisition device dedicatedly designed for the swift and accurate estimation of buried plastic pipes. The EFLM estimates the approximate locations of underground plastic pipes from the electric field data quickly, separates a GPR B-scan image into segments, keeps the segments that might contain hyperbolas, and discards the irrelevant ones. Then, the GBII model interprets the depth and radius of the buried pipe in the kept segments. Our numerical simulations and experiments prove that by utilizing the EFLM, the 1-D electric field data could be processed quickly and the GPR B-scan image could be segmented with part of irrelevant pixels discarded, while hyperbolas in the kept image segments could be automatically and accurately fitted. With our proposed model, the depth and radius of the buried pipes could be efficiently obtained.

Buried pipes beneath our city are blood vessels that feed human civilization through the supply of water, gas, electricity, and so on, and mapping the buried pipes has long been addressed as an issue. In this article, a suitable coordinate of the detected area is established, the noisy Ground Penetrating Radar (GPR) and Global Positioning System (GPS) data are analyzed and normalized, and the pipeline is described mathematically. Based on these, the Probabilistic Mixture Model is proposed to map the buried pipes, which takes discrete noisy GPR and GPS data as the input and the accurate pipe locations and directions as the output. The proposed model consists of the Preprocessing, the Pipe Fitting algorithm, the Classification Fitting Expectation Maximization (CFEM) algorithm, and the Angle-limited Hough (Al-Hough) transform. The direction information of the detecting point is added into the measuring of the distance from the point to nearby pipelines, to handle some areas where the pipes are intersected or difficult to classify. The Expectation Maximization (EM) algorithm is upgraded to CFEM algorithm that is able to classify detecting points into different classes, and connect and fit multiple points in each class to get accurate pipeline locations and directions, and the Al-Hough transform provides reliable initializations for CFEM, to some extent, ensuring the convergence of the proposed model. The experimental results on the simulated and real-world datasets demonstrate the effectiveness of the proposed model.

Ground-penetrating radar (GPR) has been widely used as a nondestructive tool for the investigation of the subsurface, but it is challenging to automatically process the generated GPR B-scan images. In this paper, an automatic GPR B-scan image interpreting model is proposed to interpret GPR B-scan images and estimate buried pipes, which consists of the preprocessing method, the open-scan clustering algorithm (OSCA), the parabolic fitting-based judgment (PFJ) method, and the restricted algebraic-distance-based fitting (RADF) algorithm. First, a thresholding method based on the gradient information transforms the B-scan image to the binary image, and the opening and closing operations remove discrete noisy points. Then, OSCA scans the preprocessed binary image progressively to identify the point clusters1with downward-opening signatures, and PFJ further validates whether the point clusters with downward-opening signatures are hyperbolic. By utilizing OSCA and PFJ, point clusters with hyperbolic signatures could be classified and segmented from other regions even if there are some connections and intersections between them. Finally, the validated point clusters are fitted into the lower parts of hyperbolas by RADF that solves fitting problems with additional constraints related to the hyperbolic central axis. By integrating these methods, the proposed model is able to extract information from GPR B-scan images automatically and efficiently. The experiments on simulated and real-world data sets demonstrate the effectiveness of the proposed model.1A point cluster is a collection of points with the same class identification.