Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

Wenping Yin; Fabian Desuer; Ziqi Liu; Naixia Mou; Weijia Li; Pedram Ghamisi; Xiao Xiang Zhu; Hao Li

cs.CVSep 28, 2026

Decoding the Disaster: Multi-Task Geospatial Reasoning with Vision-Language Models and Crowdsourced Imagery for Disaster Mapping

Authors: Wenping Yin, Fabian Desuer, Ziqi Liu, Naixia Mou, Weijia Li, Pedram Ghamisi, Xiao Xiang Zhu, Hao Li

Organizations: College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China · Professorship of Big Geospatial Data Management, School of Engineering and Design, Technical University of Munich, Munich, 85521, Bavaria, Germany · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · Helmholtz-Zentrum Dresden-Rossendorf, Responsible AI Group, 09599 Freiberg, Germany · University of Iceland, Faculty of Electrical and Computer Engineering, Reykjavik 102, Iceland · Chair of Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany · Munich Center for Machine Learning, 80333 Munich, Germany · Department of Geography, National University of Singapore, Singapore 117568, Singapore

Abstract

Crowdsourced imagery provides timely, fine-grained, street-level observations for disaster mapping, complementing conventional remote sensing imagery (RSI) during emergency response. However, such imagery is often unstructured, spatially ambiguous, and lacks reliable geographic metadata, making manual geolocalization and interpretation labor-intensive and difficult to scale. This work proposes a multi-task Geospatial Reasoning Disaster mapping framework, namely GRDisaster, to examine the potential of vision-language models (VLMs) in understanding, geolocalizing, and reasoning over crowdsourced disaster imagery. GRDisaster is built on a newly curated benchmark dataset derived from PhotoMappers, comprising 26,340 images organized into human-validated volunteered geographic information (VGI), street-view imagery (SVI), RSI cross-view triplets covering multiple disaster events from 2018 to 2024. The framework combines deterministic and probabilistic cross-view geolocalization with multi-view fusion to associate VGI images with georeferenced SVI and RSI. It introduces two sets of spatial reasoning indicators for cross-view geolocalization validation and disaster damage assessment. These indicators use structural, environmental, and global-scene cues to validate cross-view correspondences and visually observable damage evidence with expert-verified annotations to assess disaster severity, improving the interpretability of VLM outputs. To our knowledge, this study provides the first systematic investigation and unified evaluation framework for examining how VLM-based spatial reasoning can transform crowdsourced disaster imagery into actionable geospatial artificial intelligence (GeoAI) through cross-view geolocalization validation, interpretable spatial reasoning, and damage-aware severity assessment.

Figures & tables

Fig. 1: Examples from the PhotoMappers dataset: (a) crowdsourced VGI and (b) corresponding RSI across different disaster types.

Fig. 2: Overview of the three interconnected tasks in the GRDisaster framework using the VGI–SVI–RSI benchmark: deterministic and probabilistic cross-view geolocalization, and explainable VLM-based reasoning for match verification and damage severity classification.

Fig. 3: Overview of deterministic and probabilistic cross-view geolocalization.

No.	Indicator name	Evaluation focus	Dimension
			Appearance	Spatial layout	Landmark cue
1	Building type and scale	Building and structure: Building appearance consistency	$\checkmark$
2	Overall building appearance	Building and structure: Building appearance consistency	$\checkmark$
3	Building position relative to nearby roads or open space	Building and structure: Building spatial-position consistency		$\checkmark$
4	Building position and shape correspondence in SVI–RSI	Building and structure: Building spatial-position consistency		$\checkmark$
5	Recognizable building outline	Building and structure: Corresponding building landmark			$\checkmark$

TABLE I: Evaluation indicators for VLM-based interpretive reasoning of cross-view geolocalization results.

Indicator name	Damage severity
	Low	Medium	High
Flooding and water impact	Shallow or geolocalized water with limited impact on visible objects.	Clear flooding affects roads, buildings, vehicles, or public spaces.	Deep water, strong flow, storm surge, or widespread inundation.
Building and structural damage	Buildings are mostly intact , with no clear or only minor damage.	Partial damage is visible, such as doors, windows, walls, roofs, or water intrusion .	Severe structural damage, collapse , heavy inundation, or loss of building function.
Road and access disruption	Roads or paths remain mostly usable , with minor debris or shallow water.	Roads are partially blocked or difficult to access due to water, debris, vehicles, or fallen trees.	Roads are impassable or heavily disrupted by deep water, large debris, collapse, or severe surface damage.
Vegetation and natural damage	Vegetation remains intact , with only small branches or geolocalized disturbance.	Fallen trees, broken branches, or damaged vegetation partially affect roads, buildings, or open spaces.	Severe vegetation damage, large fallen trees, or widespread natural disturbance blocks access or damages objects.
Infrastructure and facility damage	Facilities are mostly intact , with only minor or geolocalized disturbance.	Some facilities are damaged or disrupted , such as poles, signs, fences, utilities, or public equipment.	Critical infrastructure or multiple facilities are severely damaged, submerged, collapsed , or nonfunctional .

TABLE II: Image-based indicators for VLM-based disaster damage severity reasoning and classification.

Fig. 4: Deterministic cross-view geolocalization performance under different retrieval and fusion settings: (a) VGI–RSI retrieval, (b) VGI–SVI retrieval, (c) SVI–RSI retrieval, and (d) VGI–SVI–RSI fusion with and without Sinkhorn re-ranking.

Method	Type	View Input	Median (km)	Mean (km)	@1km	@25km	@200km	@750km	@2500km
DINOv3-B	$\boldsymbol{\times}$	VGI–RSI	381.47	747.35	19.13	23.29	37.81	68.17	93.96
DINOv3-L	$\boldsymbol{\times}$	VGI–RSI	218.67	525.35	28.25	33.20	48.35	76.25	96.18
DINOv3-B	$\boldsymbol{\times}$	VGI–SVI	340.37	631.42	25.11	28.59	40.43	70.39	95.10
DINOv3-L	$\boldsymbol{\times}$	VGI–SVI	193.99	545.46	31.44	35.71	50.34	75.40	96.58
DINOv3-B	$\boldsymbol{\times}$	VGI–SVI–RSI	206.96	547.83	31.78	35.99	49.43	74.94	95.90
DINOv3-L	$\boldsymbol{\times}$	VGI–SVI–RSI	65.31	390.08	41.40	45.62	58.60	81.44	98.35

TABLE III: Comparison of geolocalization performance between generative and retrieval-based approaches. Median and mean are errors in kilometers, while @1,km–@2500,km are the percentages of predictions within each distance threshold.

Fig. 5: Heatmaps for (a) and (b) correctly and (c) and (d) incorrectly geolocalized imagery pairs generated by our best-performing DINOv3-L model with VGI–SVI–RSI fusion and Sinkhorn re-ranking, with the corresponding raw VGI ground-truth images included for reference. The first column shows the ground-truth images, and the second column shows the predicted results.

No.	Indicator Abbr.	VLM vs Exp. A		VLM vs Exp. B		Exp. A vs Exp. B		Qwen		GPT		InternVL
		Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$
1	Building type and scale	0.954	0.964	0.991	0.985	0.957	0.975	0.644	0.434	0.708	0.610	0.537	0.376
2	Overall building appearance	0.983	0.990	0.874	0.921	0.862	0.914	0.632	0.429	0.702	0.613	0.504	0.364
3	Building-road-open space position	0.940	0.965	0.991	0.995	0.948	0.970	0.637	0.407	0.694	0.585	0.517	0.355
4	Building shape correspondence	0.989	0.989	0.968	0.977	0.974	0.985	0.649	0.415	0.710	0.578	0.522	0.351
5	Recognizable building outline	0.991	0.990	0.991	0.990	0.994	0.997	0.633	0.426	0.724	0.630	0.556	0.396

TABLE IV: Agreement and quadratic weighted kappa (

\kappa_{w}

) evaluation for cross-view geolocalization reasoning. Bold the best-performing results in each category. Bold the best-performing results in each category.

Fig. 6: Spatial reasoning for cross-view geolocalization across disaster types. Three VLMs are compared across building and structure, road and transport, vegetation and terrain, objects and facilities, global scene (circles), and cross-view geolocalization (diamond), with different colors indicating reasoning results.

Indicator	Level	VLM vs Expert A		VLM vs Expert B		Expert A vs Expert B
		Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$	Agr.	$\kappa_{w}$
Flooding and water impact	Low	0.966	0.954	0.932	0.906	0.932	0.908
	Medium	0.940	0.963	0.940	0.948	0.949	0.953
	High	0.940	0.964	0.932	0.957	0.974	0.984
	Overall	0.949	0.968	0.934	0.953	0.952	0.964
Building and structural damage	Low	0.983	0.916	0.983	0.927	0.991	0.846

TABLE V: Agreement and quadratic weighted kappa (

\kappa_{w}

) evaluation between Gemini and two expert annotations for disaster damage severity assessment with 20% expert verification.

Indicator	Level	Qwen Agr.	Qwen $\kappa_{w}$	GPT Agr.	GPT $\kappa_{w}$	InternVL Agr.	InternVL $\kappa_{w}$
Flooding and water impact	Low	0.661	0.601	0.717	0.667	0.394	0.306
	Medium	0.789	0.851	0.772	0.844	0.583	0.462
	High	0.802	0.842	0.877	0.915	0.627	0.427
	Overall	0.764	0.831	0.777	0.848	0.551	0.465
Building and structural damage	Low	0.829	0.581	0.927	0.591	0.423	0.175
	Medium	0.735	0.757	0.764	0.810	0.239	0.161

TABLE VI: Agreement and quadratic weighted kappa evaluation of VLMs for disaster damage severity assessment using Gemini as a human annotation substitute. Bold the best-performing results in each category.

Fig. 7: Examples of VLM-based disaster damage reasoning across six dimensions. Three VLMs are compared in flooding and water impact, building and structural damage, road and access disruption, vegetation and natural damage, infrastructure and facility damage, and debris and scene disruption. The circles in each group follow this order, with different colors indicating accuracy.

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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.

Abstract

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