Today, with the rapid development of the geographic information industry, automatic road extraction from satellite imagery is a basic requirement. Most existing methods have been designed based on binary segmentation. However, these methods do not consider the topological features of road networks, which include point, edge, and direction. In this study, a topology-based multi-task convolution network is designed, namely Bi-HRNet, which can effectively learn the key features of nodes and their directions. First, the proposed network learns the node heatmap of roads, and then the pixel coordinates are extracted from the node heatmap via non-maximum suppression (NMS). At the same time, the connectivity between nodes is predicted. To improve the integrity and accuracy of connectivity, we propose a bidirectional connectivity prediction strategy, which can learn the bidirectional categories instead of direction angles. The bidirectional categories are designed based on “top-to-bottom” and “bottom-to-top” strategies, which can improve the accuracy of the connectivity between nodes. To illustrate the effectiveness of the proposed Bi-HRNet, we compare our method with several methods on different datasets. The experiments show that our method achieves a state-of-the-art performance and significantly outperforms various previous methods.

Road topology extraction from satellite images, which has long been of interest, is an essential task in remote sensing. The graph representation of road networks is one of the most challenging aspects of road topology extraction. Most existing approaches cast road extraction as binary segmentation and then use post-processing, such as skeletonization, to infer networks from pixel-wise prediction. In our work, we believe that a road network can be represented by an undirected graph denoted as G=(V,E), where V and E represent the set of road nodes and the set of edges between nodes, respectively. Thus, to construct the road topology, we propose NodeConnect, a new method of extracting nodes for a road network and inferring the connectivity between nodes. A convolutional neural network is jointly trained to predict the nodes and connectivity map for nodes, and the edges between nodes are inferred from the connectivity map. We compare our approach with several segmentation methods on the DeepGlobe and RoadTracer datasets. The experiments show that our approach achieves state-of-the-art performance in terms of pixel-based metrics and topological precision and recall.

The emergence of Gaussian Splatting has ushered in a new phase in large-scale scene geometry reconstruction. Existing research in this field primarily focuses on computational efficiency and often neglects the effective utilization of inherent structural geometric priors. This paper introduces a shape-optimized Gaussian Splatting method based on the Manhattan World Assumption for precise geometric reconstruction from drone imagery. By leveraging the constraints provided by this assumption, a geometry-driven optimization mechanism is developed to guide the deformation and distribution of each Gaussian splat. This ensures improved alignment with the dominant structural directions of the scene. Additionally, dense point clouds are generated from the optimized Gaussian representation, making them more suitable for downstream tasks. The proposed approach maintains high visual fidelity while significantly improving geometric accuracy. Extensive experiments on multiple large-scale scene datasets, including a new benchmark for Jinan City, demonstrate that the method surpasses current state-of-the-art approaches in geometry-oriented evaluation metrics by a considerable margin.

3D Gaussian Splatting (3DGS) has significantly advanced the field of visual SLAM. However, outdoor monocular SLAM systems still suffer from scale ambiguity, so recovering the absolute scale remains difficult, and tracking often becomes brittle in large scenes. To address these issues, we present MoGe2-SLAM, an outdoor monocular 3DGS-SLAM framework that incrementally recovers camera trajectories and dense maps in an approximate absolute metric scale from a monocular RGB sequence with a known camera-height prior. By approximate absolute metric scale, we mean that camera poses and the reconstructed map are expressed in meters after camera-height calibration, rather than only up to an unknown global scale. Our core idea is to anchor frozen MoGe-2 geometry to a physical camera-height/ground-plane ruler, enabling approximate absolute-scale reconstruction. Specifically, we perform Absolute-Scale Initialization on the first keyframe by replacing MoGe-2’s advertised metric global scale with a known camera height and ground-plane geometry, generating the initial Gaussian scene directly in physical units. We then adopt Grid-Balanced Tracking that encourages spatially distributed feature extraction and maintains stable tracking. For mapping, we employ an ORB-anchored strategy that aligns the MoGe-2 depth prior with the rendered depth using geometric constraints, thereby mitigating scale drift. On KITTI, Waymo, and Argoverse 2, MoGe2-SLAM is competitive with existing monocular SLAM baselines in trajectory shape and novel-view fidelity while recovering trajectories and Gaussian maps in an approximate physical scale under the stated camera-height prior.