---
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).