STEAM is a self‑training framework for unsupervised cross‑view geo‑localization that eliminates the need for image‑generation warm‑ups or clustering. It introduces a Stable Spatial‑Aware Module for bounded nonlinear spatial weighting, Elastic Matching with Bidirectional Top‑K Soft Matching and Dynamic Threshold Filtering, and Adaptive Purification that updates pseudo‑labels based on confidence, age, and expiration. On the University‑1652 dataset, STEAM outperforms other unsupervised methods in both retrieval directions and shows competitive results on SUES‑200 and DenseUAV.
arXiv:2608. 19766v1 Announce Type: cross Abstract: Self-supervised pretraining on remote sensing imagery typically treats all samples as equally informative, despite large variability in geographic and visual structure.
arXiv:2606. 30576v1 Announce Type: cross Abstract: Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.
arXiv:2608.29426v1 Announce Type: cross Abstract: Reliable semantic representations derived from city-scale 3D models are increasingly important for urban analysis, infrastructure monitoring, autonom...
arXiv:2606.16569v2 Announce Type: replace Abstract: Registering two captures of the same indoor space taken at different times underpins persistent spatial memory for robots and AR systems, yet the r...
arXiv:2608. 16658v1 Announce Type: cross Abstract: Cross-view Video Geo-localization (CVG) aims to localize ground-view videos by retrieving their corresponding geo-tagged aerial images.
arXiv:2607.20116v2 Announce Type: replace Abstract: Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, diff...
arXiv:2609.37481v1 Announce Type: new Abstract: Large-scale pervasive sensing increasingly relies on high-resolution satellite imagery, yet task-specific onboard vision is constrained by costly annot...
Global visual localization of unmanned aerial vehicles (UAVs) using remote-sensing reference maps has attracted increasing attention. However, acquisition-time and imaging-platform differences between UAV and reference imagery induce substantial cross-domain appearance and viewpoint shifts, challenging robust six-degree-of-freedom (6-DoF) pose estimation.
UniGeo is a multimodal large language model designed for text-guided drone geo‑localization, enabling the identification of target regions in large image galleries from natural‑language descriptions. It integrates geo‑semantic understanding, cross‑view semantic generation, and candidate‑level verification within a shared vision‑language framework, establishing stable correspondences among local scene elements, spatial relations, and language. A multi‑stage training strategy progressively refines geo‑semantic learning, cross‑view mapping, and fine‑grained verification, yielding significant performance gains on GeoText‑1652, with R@10 and mAP improvements of 13.59 and 2.83 percentage points respectively.
arXiv:2608. 15790v1 Announce Type: new Abstract: Crevasse mapping from uncrewed aerial vehicle (UAV) imagery matters for glaciological research and for field safety in glaciated terrain.
arXiv:2606. 13896v1 Announce Type: cross Abstract: Self-supervised geospatial foundation models (GeoFMs) learn transferable representations from remote sensing data, but their downstream behavior is difficult to characterize.
The paper introduces Aligned Consensus Teacher (ACT), a semi‑supervised framework for visible‑infrared object detection that operates under an image‑pair‑level setting with only a few labeled pairs. ACT combines Cycle‑Consistent Region Alignment, Cross‑Modal Consensus Mean‑Teacher, and Text‑Guided Cross‑Modal Instance Augmentation to address limited supervision, pseudo‑label errors, and scarce tail‑class annotations. Experiments on DroneVehicle and VEDAI demonstrate that with just 10% labeled pairs, ACT achieves 94.3% of the fully supervised mAP.