ArXiv

HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning

2026-07-16 · 17:50 UTC ·Pengcheng Zhou, Xuanyu Liu, Yanchen Yin... ·1 min read
Authors
Pengcheng Zhou, Xuanyu Liu, Yanchen Yin...
Categories
cs.CV
arXiv
https://arxiv.org/abs/2607.15255v1
PDF
https://arxiv.org/pdf/2607.15255v1

Brief

HoloGeo targets landmark bias in image geo-localization by proposing two metrics (BI, BH) and the LandmarkBias-3K benchmark to quantify bias effects. It trains an evidence-driven model using a new BF-30k dataset of structured, multi-evidence bias-free reasoning chains and multi-dimensional rewards to balance attention. According to the abstract, HoloGeo maintains IM2GPS3K/YFCC4k accuracy and outperforms open-source VLMs on LandmarkBias-3K. (Based on abstract.)

Why it matters

Defines two quantitative metrics—Bias Intensity (BI) and Bias Harmfulness (BH)—and releases the LandmarkBias-3K benchmark (3,000 images) to measure the effect of landmark-induced bias on vision-language geo-localization models.

Key details

  • Introduces HoloGeo, an evidence-driven reasoning framework trained with BF-30k (30,000 images) annotated with structured multi-evidence, bias-free reasoning chains and multi-dimensional rewards; HoloGeo preserves performance on IM2GPS3K and YFCC4k while significantly outperforming open-source VLMs on LandmarkBias-3K.
  • Authored by Pengcheng Zhou, Xuanyu Liu, Yanchen Yin, et al.; posted to arXiv on 2026-07-16 (arXiv:2607.15255v1) with a PDF available at the provided link.
Source evidence

Abstract

Recent advances in Vision-Language Models (VLMs) have significantly improved image geo-localization, yet existing models remain susceptible to landmark bias, causing them to overlook geographical cues or form spurious correlations, ultimately resulting in inaccurate localization. To systematically investigate this issue, we first design two quantitative metrics, Bias Intensity (BI) and Bias Harmfulness (BH), to characterize the impact of landmarks exerted on model reasoning, and establish a comprehensive benchmark, LandmarkBias-3K. To mitigate landmark bias, we further propose an evidence-driven reasoning framework, HoloGeo, to improve the reliability of geo-localization. HoloGeo is supported by a high-quality dataset, BF-30k, annotated with structured multi-evidence bias-free reasoning chains. By incorporating multi-dimensional rewards, HoloGeo explicitly encourages balanced attention over diverse visual cues and achieves evidence-driven joint reasoning. Extensive experiments demonstrate that HoloGeo not only maintains excellent performance on IM2GPS3K and YFCC4k but also significantly outperforms existing open-source VLMs on LandmarkBias-3K, validating its effectiveness for robust geospatial reasoning.