Road surface reconstruction is crucial for autonomous driving, providing accurate and up-to-date road geometry for navigation, safety assessment, and infrastructure maintenance. Camera-based methods have become increasingly cost-effective and scalable for this task. However, achieving high-quality large-scale reconstruction remains challenging due to inconsistent observations of the same road surface points. These inconsistencies arise both within single sessions-caused by factors such as vehicle shadows and exposure shifts-and across multiple sessions, where changes in lighting conditions and viewpoints further exacerbate the problem. To address these challenges, we propose MS-Road, a camera-based approach for large-scale road surface reconstruction with strong geometric consistency. MS-Road leveraging self-supervised learning and multi-view consistent constrain to tackle two key issues: inconsistency in road appearance across different observations and inaccurate road height localization. By enforcing spatiotemporal consistency in both geometric and visual aspects, our method produces more reliable reconstructions within and across sessions. Experiments on two public datasets and a real-world dataset demonstrate that our approach achieves robust and high-fidelity reconstruction under diverse and challenging conditions.

Relationships between geographic entities, including human-land and human-people relationships, can be naturally modelled by graph structures, and geographic network representation is an important theoretical issue. The existing methods learn geographic network representations through deep graph neural networks (GNNs) based on the i.i.d. assumption. However, the spatial heterogeneity and temporal dynamics of geographic data make the out-of-distribution (OOD) generalisation problem particularly salient. We classify geographic network representations into invariant representations that always stabilise the predicted labels under distribution shifts and background representations that vary with different distributions. The latter are particularly sensitive to distribution shifts (feature and structural shifts) between testing and training data and are the main causes of the out-of-distribution generalisation (OOD) problem. Spurious correlations are present between invariant and background representations due to selection biases/environmental effects, resulting in the model extremes being more likely to learn background representations. The existing approaches focus on background representation changes that are determined by shifts in the feature distributions of nodes in the training and test data while ignoring changes in the proportional distributions of heterogeneous and homogeneous neighbour nodes, which we refer to as structural distribution shifts. We propose a feature-structure mixed invariant representation learning (FSM-IRL) model that accounts for both feature distribution shifts and structural distribution shifts. To address structural distribution shifts, we introduce a sampling method based on causal attention, encouraging the model to identify nodes possessing strong causal relationships with labels or nodes that are more similar to the target node. This approach significantly enhances the invariance of the representations between the source and target domains while reducing the dependence on background representations that arise by chance or in specific patterns. Inspired by the Hilbert–Schmidt independence criterion, we implement a reweighting strategy to maximise the orthogonality of the node representations, thereby mitigating the spurious correlations among the node representations and suppressing the learning of background representations. In addition, we construct an educational-level geographic network dataset under out-of-distribution (OOD) conditions. Our experiments demonstrate that FSM-IRL exhibits strong learning capabilities on both geographic and social network datasets in OOD scenarios.

Parameter fine-tuning is the mainstream approach for adapting Multimodal Large Langauge Models (MLLMs) to downstream remote sensing tasks. However, such method risks degrading pre-trained knowledge and also incur significant costs. This paper argues that downstream adaptation of MLLMs essentially involves effective injection of task-specific knowledge, which does not necessarily require parameter updates. Based on this perspective, we propose a training-free knowledge injection method. By constructing a multi-task knowledge base (MTKB), the model can dynamically retrieve task-related knowledge to serve as context during inference, thereby enhancing its understanding. Specifically, we design a three-part framework. (1) Task knowledge construction: Diverse texts are unified into a key and value structure for image-text matching, forming the MTKB. (2) Two-stage retrieval: A coarse-to-fine process is employed to match query images with task knowledge in the MTKB, utilizing both unimodal and cross-modal similarity. (3) Knowledge injection: Matched knowledge is integrated into the MLLM via extended embeddings, without altering parameters. Experimental results across multiple datasets demonstrate that our method significantly enhances the image understanding capabilities of the model. Our approach achieves about 5% improvement in accuracy and related metrics across several datasets, with performance on the RSVQ-LR dataset comparable to specialized models.

Accurate identification of road intersections is the pivotal task for automatic construction of high-definition maps, particularly in unstructured scenes. Existing methods predominantly rely on single-modal data and thus show an obvious unimodal limitation, i.e., lack of contextual information. Moreover, these approaches overlook the benefits of leveraging multi-modal data fusion and representation learning that is crucial for generalizability. To this end, we propose a novel orientation-aware multi-modal learning paradigm, which formulates intersection identification as an oriented object detection task. Specifically, heterogeneous fusion is introduced to harmonize disparate data modalities, i.e., vector maps, point clouds, and vehicle trajectories, into a unified feature space. Concurrently, we present trigonometry-induced adaptive regression to elevate orientation estimation, while mitigating issues related to scale imbalance and boundary confusion through dual-objective matching with spatial adaptation. To evaluate our methodology, we assemble the first-of-its-kind multi-modal benchmark tailored for complex low-speed environments, complete with fine-grained semantic annotations for intersections. Comprehensive empirical analyses, including ablation studies, affirm both the superior performance of our proposed framework and the efficacy of its constituent modules.

Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art.