arXiv:2610.07698v1 Announce Type: new Abstract: Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmenta...
arXiv:2609.37031v1 Announce Type: new Abstract: Building extraction from optical remote sensing (RS) imagery is fundamental to urban mapping, yet existing methods are often dataset-specific and gener...
arXiv:2508. 03736v2 Announce Type: replace-cross Abstract: In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations.
arXiv:2606. 00119v1 Announce Type: cross Abstract: Reliable work zone mapping is important for connected and autonomous vehicles (CAVs) to navigate safely and smoothly through work zone areas.
arXiv:2606. 02092v1 Announce Type: cross Abstract: Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets.
The paper introduces a top‑down framework for accurately locating athletes in metric world coordinates using a single calibrated broadcast frame. It presents three main contributions: a Boundary‑Aware Adaptive Tiling method that expands tile boundaries to avoid splitting athletes across tiles, a specialized two‑keypoint estimator based on RTMPose‑X for pelvis and ground projection points, and a deterministic lift of 2D projections into 3D world coordinates via camera‑calibrated ray casting. The approach achieves a LocSim score of 97.44 and an mAP of 0.9128, surpassing the baseline by over 21 % on a public test set.
The study presents a method for estimating building heights in a large Brazilian city using freely available satellite data, including TerraSAR-X StripMap, PlanetScope, and Sentinel-1. A geographically weighted random forest model achieved an RMSE of 5.34 m and an R² of 0.756 against LiDAR reference data, with local feature importance varying by building type and context. The results highlight that no single sensor dominates across all scenarios, offering guidance for selecting satellite-derived products in different urban settings.
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency.
arXiv:2606. 11490v1 Announce Type: new Abstract: Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals.
The paper presents a method for estimating building heights in a large Brazilian city using freely available satellite data, including TerraSAR‑X StripMap, PlanetScope, and Sentinel‑1. By integrating these sources in a geographically weighted random forest, the authors achieve an RMSE of 5.34 m and an R² of 0.756 against a LiDAR reference. The study also reveals that different predictors dominate in different urban contexts, offering guidance on sensor selection for building‑height mapping.
arXiv:2606. 08920v1 Announce Type: cross Abstract: Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications.
Open-vocabulary remote sensing segmentation has recently emerged as a promising paradigm that enables pixel-level recognition of arbitrary categories specified by natural language, including classes unseen during training. However, geospatial domain shifts caused by heterogeneous regions, spatial resolutions, and acquisition platforms weaken visual-text matching and limit cross-dataset generalization.