Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· Vol XLIX-B1-2026, pp. 615-620· 0 citations· 12 references
TL;DR
This work presents urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph, and shows improved global alignment, reduced drift, and more detailed projection of local semantics into EO space.
Abstract
Abstract. Urban scene understanding requires both global geographic context and local structural detail. Earth Observation (EO) imagery supports large-scale land-cover and land-use (LCLU) mapping, but in urban areas it often merges heterogeneous surfaces into broad built-up classes. Vehicle-based sensors such as LiDAR and cameras recover these local structures, but their maps can drift and often remain in a local coordinate frame. We present urban graph, which combines overhead EO priors, vehicle observations, and fixed roadside anchors in a hierarchical semantic scene graph. Coarse georeferenced regions from EO data are updated with local observations, while a factor graph jointly optimises SLAM constraints and global geodetic constraints. The resulting graph is projected back to the overhead layer to separate coarse urban classes into finer semantic components. Experiments in CARLA show improved global alignment, reduced drift, and more detailed projection of local semantics into EO space.
Visual geo-localization seeks to enable autonomous aircraft positioning in GNSS-denied environments through large-scale image retrieval. Most existing methods, however, primarily rely on visible light images and are thus inherently sensitive to illumination changes and adverse weather conditions. Near-infrared (NIR) im...
Teng-Da Zhang, Yun-Zhou Zhang, Li Wang et al.· IEEE Transactions on Geoscie...· 0 citations
Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely on GPS for absolute positioning, yet GPS signals frequently degrade in urban canyons due to multipath propagation. Consequently, standard solutions like visual odometry s...
Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and...
Cross-view geo-localization enables GNSS-denied UAV navigation by matching a drone image against a geo-tagged satellite database, a key capability for remote sensing. Existing methods perform well on landmark benchmarks but struggle in continuous-coverage scenarios, where a drone image only partially overlaps surroundi...
HydroMap is proposed, an odometry-decoupled framework that reconstructs water surface elevation from stereo observations and integrates it with the structural map, and complements LiDAR maps with a persistent representation of the water surface for downstream navigation in inland waterways.
Zhong-Bi Luo, Yun-Jia Wang, H. Bruyninckx et al.· 0 citations
This paper presents a multimodal 3D data fusion workflow tailored for registering mobile laser scanning (MLS) point clouds to CityGML semantic building models (MLS-to-CityGML) in urban street environments. The proposed method first extracts stable footprint features from MLS fragments using a multi-band consensus filte...
Marco Antonio Ortiz Rincón, Yi-Hui Yang, Christoph Holst· ISPRS Annals of the Photogra...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
