{
  "id": 5548,
  "url": "https://arxiv.org/abs/2604.19411v1",
  "title": "GOLD-BEV: GrOund and aeriaL Data for Dense Semantic BEV Mapping of Dynamic Scenes",
  "summary": "Understanding road scenes in a geometrically consistent, scene-centric representation is crucial for planning and mapping. We present GOLD-BEV, a framework that learns dense bird's-eye-view (BEV) semantic environment maps-including dynamic agents-from ego-centric sensors, using time-synchronized aerial imagery as supervision only during training. BEV-aligned aerial crops provide an intuitive target space, enabling dense semantic annotation with minimal manual effort and avoiding the ambiguity of",
  "authors": "Joshua Niemeijer, Alaa Eddine Ben Zekri, Reza Bahmanyar, Philipp M. Schmälzle, Houda Chaabouni-Chouayakh, Franz Kurz",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-21T12:39:41.000Z",
  "fetched_at": "2026-07-14T16:31:48.876Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5548",
  "original_url": "https://arxiv.org/abs/2604.19411v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}