| --- |
| license: cc-by-nc-4.0 |
| task_categories: |
| - visual-question-answering |
| tags: |
| - satellite-imagery |
| - spatial-reasoning |
| - training-data |
| - remote-sensing |
| pretty_name: SQuID-Train (75,687 QA pairs) |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # SQuID-Train — 75,687 Quantitative Spatial Reasoning Training Pairs |
|
|
| Training companion to the **SQuID** benchmark |
| (https://huggingface.co/datasets/squid-bench-anon/SQuID), under review at the |
| NeurIPS 2026 Evaluations and Datasets Track. |
|
|
| - **75,687** question-answer pairs over **1,443** satellite images |
| - Generated by the same pipeline as the benchmark (identical question templates, |
| geometric definitions, minimum-area thresholds, and GSD handling), applied to the |
| **training partitions** of the source datasets (DeepGlobe, EarthVQA, PV) — |
| **zero image overlap** with the 2,000-question evaluation benchmark, which is |
| built exclusively from published validation/test partitions. |
| - Schema: `image`, `question`, `answer`, `type`, `tier`, `dataset`, `id` |
| (exact targets; no tolerance ranges — those are an evaluation-side concept). |
| - Tier distribution: Tier 1 = 19,845, Tier 2 = 50,509, Tier 3 = 5,333; |
| 7,146 zero-valued answers for feature-absence robustness. |
| - Files: `qvlm_benchmark_training.json`, `dataset_statistics.json`, |
| `images_and_masks/` (images + masks for the training partition). |
|
|
| ## Reference result |
|
|
| LoRA-finetuning Qwen2.5-VL-7B-Instruct on this split raises its SQuID benchmark |
| accuracy from 16.1% to 40.7% (Tier 1: 53.5%, Tier 2: 56.0%, Tier 3: 13.2%) — |
| end-to-end models can learn substantial metric perception from this data, while |
| multi-step compositional reasoning (Tier 3) remains open. |
|
|
| Research use only (CC BY-NC 4.0; imagery licenses inherited from the public source |
| datasets). |
|
|
