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metadata 
pretty_name: HoloGeo Dataset
license: cc-by-nc-4.0
language:
  - en
tags:
  - geolocation
  - geo-localization
  - vision-language-model
  - multimodal-reasoning
  - landmark-bias
  - benchmark
size_categories:
  - 10K<n<100K
configs:
  - config_name: BF-30K
    data_files:
      - split: train
        path: data/BF-30K/BF-30K.jsonl
  - config_name: LandmarkBias-3K
    data_files:
      - split: test
        path: data/LandmarkBias-3K/Test_dataset.jsonl

HoloGeo Dataset

Training Data and a Diagnostic Benchmark for Landmark-Bias Research in Image Geo-localization

🌐 Project Page · 📄 Paper · 💻 Code · 🤖 Model

📌 Dataset Overview

HoloGeo Dataset provides two core data resources for studying landmark bias in image geo-localization:

  • BF-30K: structured multi-evidence training data;
  • LandmarkBias-3K: a benchmark for diagnosing landmark-induced reasoning errors.

In image geo-localization, vision-language models may over-rely on highly salient cues such as prominent buildings, statues, textual signs, or cultural symbols, while overlooking complementary geographic evidence from the overall scene, resulting in unreliable localization. We refer to this phenomenon as landmark bias.

HoloGeo Dataset studies this problem from both training and evaluation perspectives:

Dataset Primary purpose Scale
BF-30K Trains region-level, structured, multi-evidence geographic reasoning 30k samples
LandmarkBias-3K Diagnoses model reasoning robustness in ambiguous or misleading landmark scenes 3k test images

This repository releases data resources only. Model weights are available at PPKQ/HoloGeo, and training and inference code is available at PKQZPC/HoloGeo.

📊 Landmark Bias Metrics

HoloGeo introduces two complementary metrics to quantify the impact of landmarks on model predictions and to construct and analyze LandmarkBias-3K:

  • Bias Intensity (BI): measures the anchoring strength of a landmark on landmark-induced predictions;
  • Bias Harmfulness (BH): measures whether a landmark weakens the model's relative preference for the ground-truth location.

BI characterizes the strength of landmark influence, while BH further determines whether this influence harms correct localization.

📁 Repository Contents 

HoloGeo/
└── data/
    ├── BF-30K/
    │   └── BF-30K.jsonl
    └── LandmarkBias-3K/
        ├── Test_dataset.jsonl
        └── Test_dataset_images.tar.zst

Note: Test_dataset_images.tar.zst is the accompanying image archive for LandmarkBias-3K. It is not loaded as a dataset split by load_dataset or the Dataset Viewer.

🚀 Loading the Dataset

Install Hugging Face Datasets:

pip install datasets

Load the two data configurations separately:

from datasets import load_dataset

bf30k = load_dataset(
    "PPKQ/HoloGeo",
    "BF-30K",
    split="train",
)

landmark_bias = load_dataset(
    "PPKQ/HoloGeo",
    "LandmarkBias-3K",
    split="test",
)

For image-based LandmarkBias-3K evaluation, also download and extract:

data/LandmarkBias-3K/Test_dataset_images.tar.zst

In a Linux/macOS environment with zstd installed (which provides unzstd), extract the archive with:

tar --use-compress-program=unzstd \
  -xf data/LandmarkBias-3K/Test_dataset_images.tar.zst

The image field in Test_dataset.jsonl corresponds to the filename of the extracted image.

🧩 BF-30K

BF-30K trains models to perform image geo-localization through region-level, multi-evidence reasoning, and contains 30k samples.

Fields

Field Description
image_id Image identifier in the source dataset
url Original image URL
IMG_ID Source image identifier retained for data traceability
latitude, longitude Geographic coordinates associated with the image
original_dataset Source dataset identifier
Box List of geographically discriminative regions
Box[].Box Bounding-box coordinates for one region
Box[].Analysis Analysis of the region's visual content and geographic relevance
vlm_think Structured reasoning that aggregates evidence from multiple regions
vlm_answer.country Country-level supervision target
vlm_answer.city City-level supervision target

🧭 LandmarkBias-3K

LandmarkBias-3K is a diagnostic benchmark for landmark bias and contains 3k test images.

The benchmark primarily covers the following challenging cases:

  • A landmark is visually prominent but insufficient for accurate localization;
  • Similar landmarks may appear in different countries or cities;
  • Textual, architectural, or cultural cues provide only coarse-grained geographic information;
  • A salient cue may mislead the model toward an incorrect location.

Fields

Field Description
data_source Source dataset of the sample
image Filename in the LandmarkBias-3K image archive
country, city Ground-truth country and city labels
id Unique sample identifier
lat, lon Ground-truth geographic coordinates
label Landmark-bias category

Evaluation Protocol

Accuracy is calculated from the geographic distance between the predicted location and the ground-truth coordinates:

Evaluation granularity Distance threshold
City <= 25 km
Region <= 200 km
Country <= 750 km

⚠️ Data Use, Limitations, and Responsible Use

  • BF-30K references original images only through URLs and does not redistribute image binaries. Upstream images remain subject to their original licenses, copyrights, and terms of use.
  • LandmarkBias-3K provides images for research reproduction and benchmark evaluation. Users remain responsible for complying with the relevant licenses and terms of the original data sources.
  • Model-generated analyses and reasoning are intended only for research and training, and should not be treated as independently verified geographic facts.
  • Do not use this dataset or its derived models for unlawful tracking, privacy infringement, evasion of safety measures, criminal activity, or other harmful purposes.

⚖️ License and Third-Party Materials

HoloGeo-provided annotations, reasoning texts, coordinate and metadata organization, and documentation are released under the CC BY-NC 4.0 license.

  • The license permits sharing and adaptation for non-commercial purposes. When using this dataset or its derivatives, provide attribution and cite the HoloGeo paper.
  • BF-30K provides original image URLs only and grants no copyright, license, or redistribution right for the original images.
  • LandmarkBias-3K images and their source metadata remain subject to the original licenses, copyrights, and terms of MP-16, GLDv2, and other upstream sources. This repository does not grant rights beyond those upstream terms.
  • Users are responsible for ensuring that their use complies with applicable law, institutional policies, and all upstream-source terms.

📖 Citation

If you use HoloGeo, please cite it as follows. Accepted at ACM Multimedia 2026. Citation details will be updated after publication.

@inproceedings{zhou2026hologeo,
  title     = {HoloGeo: Mitigating Landmark Bias in Geo-localization via Evidence-Driven Reasoning},
  author    = {Zhou, Pengcheng and Liu, Xuanyu and Yin, Yanchen and Li, Bobo and Wu, Shengqiong and Lee, Mong-Li and Hsu, Wynne},
  booktitle = {Proceedings of the 34th ACM International Conference on Multimedia},
  year      = {2026}
}

🙏 Acknowledgements

We thank the authors and open-source communities behind MP-16, Google Landmarks Dataset v2, GroundingDINO, Qwen2.5-VL, InternVL3, DeepSpeed, vLLM, and ModelScope Swift.