Building-change detection underpins many important applications, especially in the military and crisis-management domains. Recent methods used for change detection have shifted towards deep learning, which depends on the quality of its training data. The assembly of large-scale annotated satellite imagery datasets is therefore essential for global building-change surveillance. Existing datasets almost exclusively offer near-nadir viewing angles. This limits the range of changes that can be detected. By offering larger observation ranges, the scroll imaging mode of optical satellites presents an opportunity to overcome this restriction. This paper therefore introduces S2Looking, a building-change-detection dataset that contains large-scale side-looking satellite images captured at various off-nadir angles. The dataset consists of 5000 bitemporal image pairs of rural areas and more than 65,920 annotated instances of changes throughout the world. The dataset can be used to train deep-learning-based change-detection algorithms. It expands upon existing datasets by providing (1) larger viewing angles; (2) large illumination variances; and (3) the added complexity of rural images. To facilitate the use of the dataset, a benchmark task has been established, and preliminary tests suggest that deep-learning algorithms find the dataset significantly more challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking may therefore promote important advances in existing building-change-detection algorithms.

Built-up areas and buildings are two main targets in remote sensing research; consequently, automatic extraction of built-up areas and buildings has attracted extensive attention. This task is usually difficult because of boundary blur, object occlusion, and intra-class inconsistency. In this paper, we propose the multi-attention feature fusion HRNet, MAFF-HRNet, which can retain more detailed features to achieve accurate semantic segmentation. The design of a pyramidal feature attention (PFA) hierarchy enhances the multilevel semantic representation of the model. In addition, we develop a mixed convolutional attention (MCA) block, which increases the capture range of receptive fields and overcomes the problem of intra-class inconsistency. To alleviate interference due to occlusion, a multiscale attention feature aggregation (MAFA) block is also proposed to enhance the restoration of the final prediction map. Our approach was systematically tested on the WHU (Wuhan University) Building Dataset and the Massachusetts Buildings Dataset. Compared with other advanced semantic segmentation models, our model achieved the best IoU results of 91.69% and 68.32%, respectively. To further evaluate the application significance of the proposed model, we migrated a pretrained model based on the World-Cover Dataset training to the Gaofen 16 m dataset for testing. Quantitative and qualitative experiments show that our model can accurately segment buildings and built-up areas from remote sensing images.

Nighttime object detection poses significant challenges due to low illumination, noise, and reduced contrast, which can severely impact the performance of standard detection models. In this paper, we present NF-DETR (Night-Frequency Detection Transformer), a novel framework that leverages frequency domain information to enhance object detection in challenging nighttime environments. Our approach integrates physics-prior enhancement to improve the visibility of objects in low-light conditions, frequency domain feature extraction to capture structural information potentially lost in the spatial domain, and window cross-attention fusion that efficiently combines complementary features while reducing computational complexity, significantly improving detection performance without increasing the parameter count. Extensive experiments on two challenging nighttime detection benchmarks, BDD100K-Night and City-Night3K, demonstrate the effectiveness of our approach. Compared to strong baselines such as YOLOv8-M, YOLOv12-X, and RT-DETRv2-50, NF-DETR-L achieves improvements of up to +3.5% AP@50 and +3.7% AP@50:95 on BDD100K-Night, and +2.7% AP@50 and +1.9% AP@50:95 on City-Night3K, while maintaining competitive inference speeds. Ablation studies confirm that each proposed component contributes positively to detection performance, with their combination yielding the best results. NF-DETR offers a more robust solution for nighttime perception systems in autonomous driving and surveillance applications, effectively addressing the unique challenges of low-light object detection.

The Internet of Vehicles (IoV) operates in an environment characterized by asymmetric security threats, where centralized vulnerabilities create a critical imbalance that can be disproportionately exploited by attackers. This study addresses this imbalance by proposing a symmetrical security framework that integrates Blockchain and Federated Learning (FL) to restore equilibrium in the Vehicle–Road–Cloud ecosystem. The evolution toward sixth-generation (6G) technologies amplifies both the potential of vehicle-to-everything (V2X) communications and its inherent security risks. The proposed framework achieves a delicate balance between robust security and operational efficiency. By leveraging blockchain’s symmetrical and decentralized distribution of trust, the framework ensures data and model integrity. Concurrently, the privacy-preserving approach of FL balances the need for collaborative intelligence with the imperative of safeguarding sensitive vehicle data. A novel Cloud Proxy Re-Encryption Offloading (CPRE-IoV) algorithm is introduced to facilitate efficient model updates. The architecture employs a partitioned blockchain and a smart contract-driven FL pipeline to symmetrically neutralize threats from malicious nodes. Finally, extensive simulations validate the framework’s effectiveness in establishing a resilient and symmetrically secure foundation for next-generation IoV networks.

Remote sensing change detection involves detecting pixels that have changed from a bi-temporal image of the same location. Current mainstream change detection models use encoder-decoder structures as well as Siamese networks. However, there are still some challenges with this: (1) Existing change feature fusion approaches do not take into account the symmetry of change features, which leads to information loss; (2) The encoder is independent of the change detection task, and feature extraction is performed separately for dual-time images, which leads to underutilization of the encoder parameters; (3) There are problems of unbalanced positive and negative samples and bad edge region detection. To solve the above problems, a mutual feature-aware network (MFNet) is proposed in this paper. Three modules are proposed for the purpose: (1) A symmetric change feature fusion module (SCFM), which uses double-branch feature selection without losing feature information and focuses explicitly on focal spatial regions based on cosine similarity to introduce strong a priori information; (2) A mutual feature-aware module (MFAM), which introduces change features in advance at the encoder stage and uses a cross-type attention mechanism for long-range dependence modeling; (3) A loss function for edge regions. After detailed experiments, the F1 scores of MFNet on SYSU-CD and LEVIR-CD were 83.11% and 91.52%, respectively, outperforming several advanced algorithms, demonstrating the effectiveness of the proposed method.

Recently, the semisupervised learning for hyperspectral image classification (HIC) has attracted increasing attention. The previous semisupervised methods were usually based on reproducing kernel Hilbert spaces with a single kernel. In this paper, we propose a novel Laplacian least squares support vector machine in sum space (Lap-LSSVM-SS) for semisupervised HIC with multiscale kernels, which can simultaneously deal with the high and low frequency components of the target classification function with small and large scale kernels, respectively. Furthermore, due to its representation theorem, a noniterative algorithm based on the closed-form classification function of Lap-LSSVM-SS is provided to make a rapid semisupervised HIC possible in the multiple kernel learning framework. The proposed method can solve the multiclass problem of hyperspectral dataset directly. We further reduce the storage and computation cost of the proposed Lap-LSSVM-SS method with the random sampling strategy, i.e., Lap-LSSVM-SS-RS. We also show that the Lap-LSSVM-SS-RS method has near-optimal classification performance when the number of local Lap-LSSVM-SS machines is not too large. The experimental results on two real hyperspectral datasets demonstrate that Lap-LSSVM-SS-RS yields superior classification performance over the conventional semisupervised classifiers (e.g., LapSVM, S4VM, and BagSVMs, etc.) in challenging small training labeled samples and a large amount of unlabeled samples.