Light detection and ranging (LiDAR) point cloud denoising is critical for reliable environmental perception in autonomous driving and robotics. To overcome the lack of real-noise datasets and the limited generalization of algorithms that rely on synthetic data, we construct a real-world LiDAR denoising dataset with noise-clean pairs, named RealLiD. Meanwhile, we propose a dual-heterogeneous-encoder network (DHE-Net) tailored for real-world noise. DHE-Net leverages spatial order information obtained from Knearest neighbor (KNN) sampling. It employs heterogeneous dual encoders to extract both the central semantic details and boundary distribution features of point cloud patches, thereby enabling more effective denoising. Experiments on RealLiD demonstrate that DHE-Net substantially outperforms mainstream denoising algorithms across multiple metrics, including chamfer distance, thereby proving its robustness and practicality under real-world noise conditions. The dataset and code will be released as open-source after publication to support future research.

In recent years, low-cost LiDAR has gained attention for its cost-effectiveness, but the noisy point cloud data it captures limits algorithm performance. We propose the R2R-LPCD dataset, a real-world LiDAR-based point cloud denoising dataset designed to address the limitations of synthetic data in capturing complex noise patterns. Comprising 162 high-quality sample pairs, R2R-LPCD uniquely reflects real-world noise characteristics, such as ray-like noise at object boundaries and occlusion-induced structural gaps, offering a robust platform for algorithm evaluation under practical conditions. This dataset supports advancements in sensor systems, embedded AI, and real-world applications by providing tools and benchmarks for resource-efficient machine learning and edge computing. By publicly releasing R2R-LPCD, we aim to drive innovation in low-cost LiDAR applications, particularly in autonomous driving and robotics, while addressing current technical challenges through future scalability and methodological improvements.

Remote sensing change detection (RSCD) focuses on identifying regions that have undergone changes between two remote sensing images captured at different times. Recently, convolutional neural networks (CNNs) have shown promising results in the challenging task of RSCD. However, these methods do not efficiently fuse bitemporal features and extract useful information that is beneficial to subsequent RSCD tasks. In addition, they did not consider multilevel feature interactions in feature aggregation and ignore relationships between difference features and bitemporal features, which thus affects the RSCD results. To address the above problems, a difference-guided multiscale aggregation attention network, DGMA2-Net, is developed. Bitemporal features at different levels are extracted through a Siamese convolutional network and a multiscale difference fusion module (MDFM) is then created to fuse bitemporal features and extract, in a multiscale manner, difference features containing rich contextual information. After the MDFM treatment, two difference aggregation modules (DAMs) are used to aggregate difference features at different levels for multilevel feature interactions. The features through DAMs are sent to the difference-enhanced attention modules (DEAMs) to strengthen the connections between bitemporal features and difference features and further refine change features. Finally, refined change features are superimposed from deep to shallow and a change map is produced. In validating the effectiveness of DGMA2-Net, a series of experiments are conducted on three public RSCD benchmark datasets (LEVIR-CD, BCDD, and SYSU-CD). The experimental results demonstrate that DGMA2-Net surpasses the current eight state-of-the-art methods in RSCD. Our code is released at https://github.com/yikuizhai/DGMA2-Net.

Change detection (CD) is a process of extracting changes on the Earth’s surface from bitemporal images. Current CD methods that use high-resolution remote sensing images require extensive computational resources and are vulnerable to the presence of irrelevant noises in the images. In addressing these challenges, a comparison-based attention Siamese network (CAS-Net) is proposed. The network utilizes contrastive attention modules (CAMs) for feature fusion and employs a classifier to determine similarities and differences of bitemporal image patches. It simplifies pixel-level CDs by comparing image patches. As such, the influences of image background noises on change predictions are reduced. Along with the CAS-Net, an unmanned aerial vehicle (UAV) similarity detection (UAV-SD) dataset is built using high-resolution remote sensing images. This dataset, serving as a benchmark for CD, comprises 10000 pairs of UAV images with a size of$256 \times 256$. Experiments of the CAS-Net on the UAV-SD dataset demonstrate that the CAS-Net is superior to other baseline CD networks. The CAS-Net detection accuracy is 93.1% on the UAV-SD dataset. The code and the dataset can be found athttps://github.com/WenbaLi/CAS-Net.

UAVs can collect vehicle data in high-altitude scenes, playing a significant role in intelligent urban management due to their wide of view. Nevertheless, the current datasets for UAV-based vehicle detection are acquired at altitude below 150 meters. This contrasts with the data perspective obtained from high-altitude scenes, potentially leading to incongruities in data distribution. Consequently, it is challenging to apply these datasets effectively in high-altitude scenes, and there is an ongoing obstacle. To resolve this challenge, we developed a comprehensive vehicle dataset named LH-UAV-Vehicle, specifically collected at flight altitudes ranging from 250 to 400 meters. Collecting data at higher flight altitudes offers a broader perspective, but it concurrently introduces complexity and diversity in the background, which consequently impacts vehicle localization and recognition accuracy. In response, we proposed the pyramid dual pooling attention path aggregation network (PDPA-PAN), an innovative framework that improves detection performance in high-altitude scenes by combining spatial and semantic information. Object attention integration in both spatial and channel dimensions is aimed by the pyramid dual pooling attention module (PDPAM), which is achieved through the parallel integration of two distinct attention mechanisms. Furthermore, we have individually developed the pyramid pooling attention module (PPAM) and the dual pooling attention module (DPAM). The PPAM emphasizes channel attention, while the DPAM prioritizes spatial attention. This design aims to enhance vehicle information and suppress background interference more effectively. Extensive experiments conducted on the LH-UAV-Vehicle conclusively demonstrate the efficacy of the proposed vehicle detection method. Our code and dataset can be found at https://github.com/yikuizhai/PDPA-PAN.

Remote sensing change detection (RSCD) holds significant prominence as a research topic within the realm of computer vision. However, previous RSCD datasets have been constructed based on satellite remote sensing images. Traditional satellite remote sensing images have problems such as insufficient resolution, difficult data acquisition, and complex processing processes, and there is a certain gap between data distribution and actual needs. UAVs not only have the advantages of flexibility and high-speed, but also can capture high-resolution images, which are especially suitable for high-precision RSCD in small areas. Therefore, this paper proposed a new UAV RSCD dataset — UAV Building Change Detection Dataset (UAV-BCD). The proposed dataset contains 2024 pairs of finely registered high-resolution images collected by UAVs and their corresponding pixel-level labels, which can provide a new benchmark for RSCD. We evaluate the effectiveness of UAV-BCD with the five state-of-art deep neural networks in RSCD.

Change detection refers to extract change information using deep learning or traditional image processing methods to quantitatively analyze and characterize landmark changes on bi-temporal images. Currently, change detection is mainly a pixel-level task, and obtaining accurate change detection segmentation predictions requires a more elaborate and complex model architecture design. To simplify the change detection task, we proposed a novel similarity detection model, Similarity Attention Siamese Network (SAS-NET). It analyzed and predicted if the bi-temporal image patches were similar, and simplified pixel-level change detection tasks to patch-level similarity classification prediction tasks. In this work, a UAV Similarity Detection Dataset (UAV-SD) was also proposed to explore the advantages of patch-level prediction tasks over pixel-level change detection tasks. The proposed method achieved 90.5% accuracy on UAV-SD, which proves that it is more effective than other advanced change detection methods.