众包灾害影像在传统灾害制图滞后或不完整时，提供了及时的实地观测数据；然而，其地理价值取决于观测产生的地点、解读方式以及能否被可靠地定位为可操作的地理空间情报（GEOINT）。本研究利用 PhotoMappers 这一众包灾害制图项目超过十年的数据，考察灾害信息地理格局的变化，并探讨新兴 GeoAI 模型与人类地理解读之间的差异。我们结合纵向空间分析与基于街景图像（SVI）和遥感图像（RSI）的多视角地理定位，通过涵盖地理线索与推理证据的 30 项指标评估视觉-语言模型（VLMs）的推理能力。结果显示，受灾害类型、社区生命线及运营优先级变化的影响，该档案在地理和时间分布上呈现不均衡性。将志愿地理信息（VGI）、SVI 和 RSI 与 Sinkhorn 重排序相结合，实现了 40.90% 的 R@1、61.10% 的 R@5 和 69.45% 的 R@10，但年度 R@1 波动显著，介于 25.00% 至 65.07% 之间。地理推理表现同样不均：Qwen3.6-27B 的总体一致率为 67%，高于 InternVL3-8B 的 51%；两个模型在处理局部且具地方特异性的证据时，均比处理广泛空间配置时面临更大困难。我们认为，GeoAI 的价值不在于取代志愿地理解读，而在于对其进行补充，即在扩大候选检索规模的同时，保留人类对模糊及关键案例的判断。这为构建更以人为本、负责任的灾害制图 GeoAI 提供了路径。所有数据和代码均可在 https://github.com/bobleegogogo/PhotoMappers-VLM 公开获取。

Crowdsourced disaster imagery provides timely, ground-level observations when conventional disaster mapping is delayed or incomplete, yet its geographic value depends on where observations are produced, how they are interpreted, and whether they can be reliably located as actionable Geospatial Intelligence (GEOINT). Using more than a decade of PhotoMappers, a crowdsourced disaster-mapping initiative, this study examines the changing geography of disaster information and asks how emerging GeoAI models compare with human geographic interpretation. We combine longitudinal spatial analysis with cross-view geolocalization using street view imagery (SVI) and remote sensing imagery (RSI), and evaluate vision-language models (VLMs) reasoning through 30 indicators spanning geographic cues and reasoning evidence. The results reveal a geographically and temporally uneven archive shaped by changing disaster types, Community Lifelines, and operational priorities. Integrating VGI, SVI, and RSI with Sinkhorn re-ranking achieves 40.90% R@1, 61.10% R@5, and 69.45% R@10, while yearly R@1 varies markedly from 25.00% to 65.07%. Geographic reasoning is similarly uneven: Qwen3.6-27B achieves an overall agreement of 67% compared with 51% for InternVL3-8B, with both models showing greater difficulty with localized and place-specific evidence than broad spatial configurations. We argue that the value of GeoAI lies less in replacing volunteered geographic interpretation than in complementing it, scaling candidate retrieval while retaining human judgment for ambiguous and consequential cases. This provides a pathway toward more human-centered and responsible GeoAI for disaster mapping. All data and code are openly available at https://github.com/bobleegogogo/PhotoMappers-VLM.