Robust segmentation in adverse weather conditions is crucial for autonomous driving. However, these scenes struggle with recognition and make annotations expensive, resulting in poor performance. As a result, the Segment Anything Model (SAM) was recently proposed to finely segment the spatial structure of scenes and to provide powerful prior spatial information, thus showing great promise in resolving these problems. However, SAM cannot be applied directly for different geographic scales and non-semantic outputs. To address these issues, we propose SAM-EDA, which integrates SAM into an unsupervised domain adaptation mean-teacher segmentation framework. In this method, we use a “teacher-assistant” model to provide semantic pseudo-labels, which will fill in the holes in the fine spatial structure given by SAM and generate pseudo-labels close to the ground truth, which then guide the student model for learning. Here, the “teacher-assistant” model helps to distill knowledge. During testing, only the student model is used, thus greatly improving efficiency. We tested SAM-EDA on mainstream segmentation benchmarks in adverse weather conditions and obtained a more-robust segmentation model.

ICESat-2 can obtain high-precision three-dimensional measurement information of targets and has a unique advantage in determining global elevation control points. However, due to the influence of the atmospheric environment, target characteristics, hardware equipment, and other factors, its elevation accuracy is not highly reliable, and not all data points can be used as control points. To obtain high-precision elevation control points from ICESat-2 data products, an extraction strategy that combines the accuracy and location requirements for control points was developed based on the ATL08 product. Multiple attribute parameters were incorporated into the extraction procedure, including terrain factor information, segment elevation information, cloud confidence flag, surface coverage data, topographic photon quantity, and photon height difference information. The extraction approach was then performed and analyzed using experimental data from the Hanzhong area in Shanxi province and the Songshan area in Henan province. In the experiments, the root-mean-square error (RMSE) of the extracted data points in two areas were both about 0.5 m and could reach 0.3 m after eliminating gross error points caused by surface changes and misclassification. The results suggest that the developed strategy takes into account the point location requirements of control points, can overcome the influence of complex terrain and ground objects and significantly improve the overall accuracy of obtained control points. Therefore, the proposed extraction strategy can support the establishment of the global elevation control point database and promote the application of ICESat-2 data in land surface surveying and mapping.

The lack of data samples has become a bottleneck for the performance improvement of bridge detection model training via transfer learning. This article proposes a deep learning bridge detection algorithm aided by a traditional bridge detection method to alleviate this problem. First, we explore the theoretical constraints on the integration of prior-information feature maps extracted via the traditional unsupervised method. Second, we design a prior-information auxiliary module (PAM) to receive and inject prior information. Finally, we introduce this module into different kinds of object detection networks for testing. The experimental results show that prior masks generated through multiextremal segmentation can assist object detectors in many respects. In addition, the PAM can use a few prior-information feature maps to enhance the performance of four representative object detection models on the bridge recognition dataset from the 2020 Gaofen challenge on automated high-resolution earth observation image interpretation. Our method preserves the validity of end-to-end training, making the traditional method more valuable in the artificial intelligence context.

The accurate detection of relevant vehicles, pedestrians, and other targets on the road plays a crucial role in ensuring the safety of autonomous driving. In recent years, object detectors based on Transformers or CNNs have achieved excellent performance in the fully supervised paradigm. However, when the trained model is directly applied to unfamiliar scenes where the training data and testing data have different distributions statistically, the model’s performance may decrease dramatically. To address this issue, unsupervised domain adaptive object detection methods have been proposed. However, these methods often exhibit decreasing performance when the gap between the source and target domains increases. Previous works mainly focused on utilizing the style gap to reduce the domain gap while ignoring the content gap. To tackle this challenge, we introduce a novel method called IDI-SCD that effectively addresses both the style and content gaps simultaneously. Firstly, the domain gap is reduced by disentangling it into the style gap and content gap, generating corresponding intermediate domains in the meanwhile. Secondly, during training, we focus on one single domain gap at a time to achieve inter-domain invariance. That is, the content gap is tackled while maintaining the style gap, and vice versa. In addition, the style-invariant loss is used to narrow down the style gap, and the mean teacher self-training framework is used to narrow down the content gap. Finally, we introduce a multiscale fusion strategy to enhance the quality of pseudo-labels, which mainly focus on enhancing the detection performance for extreme-scale objects (very large or very small objects). We conduct extensive experiments on four mainstream datasets of in-vehicle images. The experimental results demonstrate the effectiveness of our method and its superiority over most of the existing methods.

In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene’s structure. After introducing radiance supervision, NeRF can adjust the geometry in the fixed-ray direction, resulting in a smaller search space and higher robustness. Considering the lack of NeRF construction methods for aerial scenarios, we propose a new NeRF point sampling method, which is generated using a UAV imaging model, compatible with a global geographic coordinate system, and suitable for a UAV view. We found that NeRF is optimized entirely based on the radiance while ignoring the direct geometry constraint. Therefore, we designed a radiance correction strategy that considers the incidence angle. Our method can complete point sampling in a UAV imaging scene, as well as simultaneously perform digital surface model construction and ground radiance information recovery. When tested on self-acquired datasets, the NeRF variant proposed in this paper achieved better reconstruction accuracy than the original NeRF-based methods. It also reached a level of precision comparable to that of traditional photogrammetry methods, and it is capable of outputting a surface albedo that includes shadow information.

Test-time adaptation (TTA) has been proven to effectively improve the adaptability of deep learning semantic segmentation models facing continuous changeable scenes. However, most of the existing TTA algorithms lack an explicit exploration of domain gaps, especially those based on visual domain prompts. To address these issues, this paper proposes a self-prompt strategy based on uncertainty, guiding the model to continuously focus on regions with high uncertainty (i.e., regions with a larger domain gap). Specifically, we still use the Mean-Teacher architecture with the predicted entropy from the teacher network serving as the input to the prompt module. The prompt module processes uncertain maps and guides the student network to focus on regions with higher entropy, enabling continuous adaptation to new scenes. This is a self-prompting strategy that requires no prior knowledge and is tested on widely used benchmarks. In terms of the average performance, our method outperformed the baseline algorithm in TTA and continual TTA settings of Cityscapes-to-ACDC by 3.3% and 3.9%, respectively. Our method also outperformed the baseline algorithm by 4.1% and 3.1% on the more difficult Cityscapes-to-(Foggy and Rainy) Cityscapes setting, which also surpasses six other current TTA methods.