Electroencephalography (EEG) is a widely used, noninvasive, and portable neuroimaging technique with high temporal resolution. However, EEG recordings are inherently mixtures of neural signals and artifacts, making artifact separation and removal essential yet challenging. Various blind source separation (BSS) methods, particularly those based on independent component analysis (ICA), have been broadly applied to EEG but still face limitations, as artifacts often remain mixed with neural sources within the same components. To address this issue, we propose a novel framework based on independent lowrank matrix analysis (ILRMA), which captures distinct spectral structures and temporal dynamics across different source types. By unifying ILRMA's two three-dimensional outputs into a four-dimensional representation, the proposed method extends conventional component-level analysis to intra-component-level, unfolding individual components along the frequency dimension to multiple intra-components with more discriminative patterns. By this means, signal sources, including artifacts, are finely separated with less neural information loss and residual noise, which also builds a solid base for enhancing the reliability and reproducibility of downstream EEG analyses.

In this paper, we propose a new large scale dataset dedicated to fine-grained building extraction in Chinese regions. To ensure the representativeness of the dataset samples, various factors are taken into consideration, encompassing geographical distribution, appearance variety, rural areas and spatial layout. Consequently, the dataset is composed of 131 K building instances, covering an expansive area of 784.3 square kilometers across 34 cities. Each building is represented with a polygon and a corresponding rooftop type. The rooftops are categorized into 12 fine-grained types. Moreover, we conduct experiments using four classical methods on this dataset to establish baselines for future studies. This dataset will promote the refined development of the city management and planning by providing essential resources for developing the state-of-the-art methods to accurately identify building instances on a city or national scale.

In the era of deep learning, training data play an essential role. Yet, their generation is expensive concerning both cost and time. Besides this, it often suffers from (1) insufficient data, (2) fixed definition of categories possibly leading to data imbalance, and (3) difficult quality control of manual annotations. In this paper, we propose an approach for automatic generation of urban building data for detection and classification from remote sensing imagery which attempts to deal with these issues. Datasets in popular format, which can be directly used for training, are created in a fully automatic pipeline from the raw data. Furthermore, the generation process can be customized to adapt the datasets to specific tasks as well as optimized according to the data characteristics of the source. We show that an automatically generated dataset can reach a comparable level to the current open datasets concerning both the quantity of instances as well as the diversity of categories. All this is achieved in a few hours without manual intervention or costs for annotation. Experiments with multiple datasets including the comparison with manually annotated data demonstrate the potential of the proposed approach.

Training data is an essential ingredient for the development of deep learning approaches. Yet, the preparation of training datasets for building detection and classification in remote sensing images implies substantial manual work and is, therefore, expensive concerning both labor charges and time. Since manual annotation also strongly depends on the experience and expertise of the annotators, quality control is an unavoidable issue. It is, thus, of great interest to explore means to reduce the manual part of dataset generation while keeping the quality of the annotation at an acceptable level.In this paper, we present a novel approach to creating training datasets for individual building detection and classification from remote sensing imagery consisting of a fully automatic pipeline. Using 3D city models and high-resolution imagery as input, annotations including building footprint and their attributes are automatically generated and combined with the corresponding image segments into a standard dataset complying with the COCO format. Experiments comprising also the comparison to manually labeled datasets demonstrate the potential of the proposed work.

Datasets play a key role in developing superior building detection approaches. However, most of the previous work focuses on accurate building masks and scale expansion, while the categories are always missing, which hinders the further analysis of urban development and cultures. Therefore, we propose a benchmark for building detection and fine-grained classification from very high-resolution (VHR) satellite imagery. An extensive annotation is performed for about 0.5 million building instances with 12 fine-grained roof types and individual polygons. The annotation of building functions of two cities in the previous version (UBCv1) [1] is also integrated. To ensure the building variety, it consists of VHR optical images of 20 unique cities worldwide with various landforms and styles of architecture. Its variety and fine-grained categories pose great challenges and meanwhile provide a foundation for the building extraction and fine-grained classification on a global scale. Besides, 17 cities are provided with finely aligned Synthetic Aperture Radar (SAR) images, which can be employed for the development and evaluation of approaches optionally based on optical, SAR, or multi-modal images. Significantly, the proposed benchmark is used as the base of the 2023 IEEE GRSS Data Fusion Contest [2]. The dataset and codes of the baseline methods are available at: https://github.com/AICyberTeam/UBC-dataset/tree/UBCv2.

In this paper we present a robust and efficient approach for automatic urban scene classification based on imagery and elevation data. Scene classification is of great interest for a broad spectrum of applications, e.g., city models, urban planning and land cover/use. Because of the availability of high resolution imagery and the corresponding scene complexity as well as heterogeneous appearance of objects, scene classification of urban areas is still challenging with respect to accuracy and efficiency. To this end, we propose "relative features", which are intra-class stable and inter-class discriminative, instead of absolute ones for color and geometry to deal with object diversity and scene complexity. The proposed approach provides a pixel-wise as well as a patch-wise schemes with (1) robustness against the variability of object appearance, (2) adaptation to undulating terrain and (3) fully-parallel processing for feature extraction and classification. Experiments on public benchmark and self-acquired data demonstrate the potential of the proposed approach.

This paper presents an original approach to dynamic anomalous behavior detection in individual trajectory using a recursive Bayesian filter. The anomalous pattern detection is of great interest for navigation, driver assistance systems, surveillance as well as crisis management. In this work, we focus on the GPS trajectories of automobiles finding where the driver’s behavior shows anomalies. Such anomalous behaviors can happen in many cases, especially when the driver encounters orientation problems, i.e., taking a wrong turn, performing a detour, or losing the way. First, three high-level features, i.e., turns and their density, detour factor, and route repetition are extracted from the given trajectory geometry, for which a long-term perspective is required to observe data sequences of a significant length instead of individual time stamps. We therefore employ high-order Markov chains with a ‘dynamic memory’ to model the trajectory integrating these long-term features. The Markov model is processed by a proposed recursive Bayesian filter to infer an optimal probability distribution of the potential anomalous driving behaviors dynamically over time. The filter performs unsupervised detection in single trajectories based on local features only. No training process is required to characterize the anomalous behaviors. By analyzing the detection results of individual trajectories, collective behaviors can be derived indicating traffic issues such as congestions and turn restrictions. Experiments are performed on volunteered geographic information (VGI) data, self-acquired trajectories, and open trajectory datasets to demonstrate the potential of the proposed approach.

This paper presents a generative statistical approach to 3D building roof reconstruction from airborne laser scanning point clouds. In previous works bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Since the laser scanning data of urban scenes often contain extra structures and artefacts due to tree clutter, reflection from windows, water features, etc., bottom-up reconstructions may result in a number of incomplete or irregular roof parts.