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原文摘要

Vision-Language Models (VLMs) describe satellite images fluently but struggle with the quantitative spatial reasoning that remote sensing (RS) analysts perform routinely, such as counting buildings, measuring forest area in hectares and computing distances in meters. We introduce SQuID (Satellite Quantitative Intelligence Dataset), a benchmark of 2,000 questions over 587 images from four sources at 0.3 to 1.0 m ground sampling distance, covering 24 question types in three difficulty tiers. Answers are computed from curated segmentation masks or, for 50 questions, from human consensus, and numeric answers are scored against tolerance ranges informed by human annotations. SQuID also releases the reference masks behind its answers, so that modular systems can be evaluated using reference rather than predicted masks, and 75,687 training question-answer pairs on disjoint images (SQuID-Train). Among 15 evaluated systems, the best zero-shot end-to-end VLM reaches 28.1%, and RS-specialized VLMs score lower. As a baseline, we propose QASR (Quantitative Agentic Spatial Reasoning), in which an LLM writes a program that segments the image and measures the masks with geometric operators. QASR reaches 42.0%. On the 1,950 mask-derived questions, running its programs on the reference masks instead of predicted masks raises accuracy from 42.2% to 82.6%, so segmentation is its main source of error. Fine-tuning Qwen2.5-VL-7B on SQuID-Train raises its accuracy from 16.1% to 40.7%, close to QASR. Multi-condition questions (Tier 3) remain the hardest in every setting, at 62.0% for QASR with reference masks and 13.2% after fine-tuning. These results leave clear room for end-to-end models that learn metric spatial reasoning, and SQuID provides the data to train and evaluate them.