arXiv AI
What Does CLIP Learn for Regional Geolocalization? Probing Visual Cues and Scene Configuration After Adaptation
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arXiv Computer Vision
Oct 15
Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework
arXiv:2603.08491v3 Announce Type: replace Abstract: Cross-modal Geo-localization (CMGL) matches ground-level text descriptions with geo-tagged aerial imagery, which is crucial for pedestrian navigati...
arXiv Computer Vision
6d ago
LoCA: Enabling Multi-Modality in Geospatial Location Encoders
arXiv:2610.32894v1 Announce Type: new Abstract: Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery. We pr...
arXiv Computer Vision
Aug 27
OpenCVL: An Open, Diverse, and Large-Scale Dataset for Fine-Grained Cross-View Localization
OpenCVL is a large, open dataset for fine-grained cross-view localization, comprising 617,388 ground‑aerial image pairs from 41 European cities. It blends high‑end sensor data with diverse in‑the‑wild images and includes a curation framework to correct pose annotations, enabling reliable evaluation. The dataset also offers cross‑area and snowy test sets to probe generalization, and experiments show that adding noisy in‑the‑wild data improves model performance on clean tests.
arXiv Computer Vision
Aug 25
GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training
arXiv:2607.02486v2 Announce Type: replace Abstract: Descriptor-free visual localization eliminates high-dimensional descriptor storage, preserves scene privacy, and simplifies map maintenance, yet it...
Hugging Face Trending Papers
Jul 22
RIM: A Retrieval-In-Matching Framework for Cross-Domain Global Visual Localization of UAVs
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.
arXiv Computer Vision
Sep 2
You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change
Vision‑language models used to gauge urban change from repeated street‑level images exhibit limited reliability at single locations. In a study of 4,648 image pairs from 435 Google Street View points across five U.S. cities, re‑photographing the same street altered perception scores by an average of 0.80 points—about two‑thirds of the difference between distinct streets—while repeated model calls added negligible variation. Although image re‑encoding, prompt order, and various image statistics contributed modestly, a small systematic drift (~0.1 points) persisted and grew with time between captures, suggesting minor unrecorded physical changes. Controlled experiments revealed that varying camera and image properties can shift scores, and that camera geometry alone caused a model to falsely report change in 45% of identical scenes; normalising to a common virtual camera reduced this to 7.5%. Despite these individual‑point unreliabilities, aggregating many paired observations recovers a clear redevelopment signal, indicating that such models are dependable at large scales but not for single‑location assessments.