--- 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 # HoloGeo Dataset ### Training Data and a Diagnostic Benchmark for Landmark-Bias Research in Image Geo-localization [🌐 Project Page](https://hologeo.github.io/) · [📄 Paper](https://arxiv.org/abs/2607.15255) · [💻 Code](https://github.com/PKQZPC/HoloGeo) · [🤖 Model](https://huggingface.co/PPKQ/HoloGeo) --- ## 📌 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](https://huggingface.co/PPKQ/HoloGeo), and training and inference code is available at [PKQZPC/HoloGeo](https://github.com/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 ```text 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: ```bash pip install datasets ``` Load the two data configurations separately: ```python 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: ```text data/LandmarkBias-3K/Test_dataset_images.tar.zst ``` In a Linux/macOS environment with `zstd` installed (which provides `unzstd`), extract the archive with: ```bash 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](https://creativecommons.org/licenses/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. ```bibtex @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.