Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning

Yushuo Zheng; Huiyu Duan; Zicheng Zhang; Xiaohong Liu; Xiongkuo Min

cs.CVMar 11, 2026

Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning

Authors: Yushuo Zheng, Huiyu Duan, Zicheng Zhang, Xiaohong Liu, Xiongkuo Min

Organizations: Shanghai Jiao Tong University · Shanghai Artificial Intelligence Laboratory

Abstract

Geolocation, the task of identifying the geographic location of an image, requires abundant world knowledge and complex reasoning abilities. Though advanced large multimodal models (LMMs) have shown superior aforementioned capabilities, their performance on the geolocation task remains unexplored. To this end, we introduce WanderBench, the first open access global geolocation benchmark designed for actionable geolocation reasoning in embodied scenarios. WanderBench contains over 32K panoramas across six continents, organized as navigable graphs that enable physical actions such as rotation and movement, transforming geolocation from static recognition into interactive exploration. Building on this foundation, we propose GeoAoT (Action of Thought), a Geolocation framework with Action of Though, which couples reasoning with embodied actions. Instead of generating textual reasoning chains, GeoAoT produces actionable plans such as, approaching landmarks or adjusting viewpoints, to actively reduce uncertainty. We further establish an evaluation protocol that jointly measures geolocation accuracy and difficulty-aware geolocation questioning ability. Experiments on 19 large multimodal models show that GeoAoT achieves superior fine-grained localization and stronger generalization in dynamic environments. WanderBench and GeoAoT define a new paradigm for actionable, reasoning driven geolocation in embodied visual understanding.

Figures & tables

Dataset	Size	Geographic Coverage	Source	Open Access	Navigation Support	Bidirectional Evaluation
Google-WS [ 11 ]	15k	Global	Map Service	✗	✗	✗
GMCP [ 40 ]	105K	Local	Map Service	✗	✗	✗
StreetCLIP [ 17 ]	1M	Unknown	Map Service	✗	✗	✗
Im2GPS [ 19 ]	237	Local	Web-Scraped	✓	✗	✗
Im2GPS3K [ 36 ]	2997	Local	Web-Scraped	✗	✗	✗
YFCC4K citevo2017	4536	Local	Web-Scraped	✗	✗	✗

Table 1: Comparison of geolocation datasets. WanderBench is the only openly available, globally scoped dataset providing both navigation support and bidirectional geolocation evaluation.

Figure 2: Figure 2. (a) Global distribution of model-proposed locations across all continents. (b) Country-level distribution of the proposed locations. (c) Average navigation-graph structure of the WanderBench dataset.

Model	$O_{16}$	$H_{16}$	$A_{\text{hull}}$	$R_{\text{CE}}$	$\bar{r}_{\text{NN}}$
Model	( $\times 10^{-2}$ )	( $\times 10^{-1}$ )	( $\times 10^{-2}$ )	( $\times 10^{-1}$ )	( $\times 10^{-2}$ )
InternVL2-76B [ 9 ]	2.73	3.10	8.61	3.21	4.63
InternVL2.5-38B [ 8 ]	1.82	2.50	5.12	1.27	1.84
InternVL2.5-78B [ 8 ]	1.95	2.58	4.31	1.63	2.35
InternVL3-38B [ 32 ]	2.02	2.66	7.16	1.95	2.82
InternVL3-78B [ 32 ]	2.08	2.62	5.40	1.97	2.84

Table 2: Spatial diversity metrics of model-proposed locations. All coordinates normalized to

[0,1]^{2}

per continent.

O_{16}

: occupancy on

16{\times}16

grid;

H_{16}

: entropy;

A_{\text{hull}}

: convex-hull area;

R_{\text{CE}}

: Clark–Evans ratio;

\bar{r}_{\text{NN}}

: mean nearest-neighbor distance.

Figure 3: Visualization of the average navigation graph structure across all locations in the WanderBench dataset. Node sizes correspond to occurrence frequency within spatial bins, while edge thickness indicates transition frequency between bins.

Model Type	Model	Overall		Street			City			Country
		Dist. $\downarrow$	Score $\uparrow$	Acc. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$	Acc. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$	Acc. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$
Retrieval Based	GeoCLIP [ 35 ]	1925	65.81	0.158	0.158	0.158	0.302	0.302	0.302	0.611	0.611	0.611
	GeoCLIP-top5	1403	73.55	0.232	0.233	0.233	0.392	0.392	0.392	0.700	0.700	0.700
Open Source LMMs	InternVL2.5-78B [ 8 ]	1124	74.69	0.164	0.141	0.152	0.368	0.316	0.340	0.723	0.668	0.694
	InternVL3-78B [ 32 ]	1441	72.77	0.171	0.148	0.159	0.359	0.309	0.332	0.697	0.646	0.670
	Qwen2VL-72B [ 37 ]	988.7	83.46	0.270	0.233	0.250	0.525	0.451	0.485	0.837	0.813	0.825

Table 3: Baseline performance of models on the benchmark. Lower is better for Distance (km), higher is better for all other metrics. Blue bold values represent the best within the open-source model type. Red bold values represent the best performance across all model types (closed-source).

Model Type	Model Name	$F\,\uparrow$ Stat.	$p\,\downarrow$ $(10^{-2})$	$\rho\,\uparrow$ Spear	$p\,\downarrow$ Spear	Slope $\uparrow$ OLS	$p\,\downarrow$ OLS	Avg. Score Hard	Avg. Score Medium	Avg. Score Easy	Remark
Open Source	InternVL2-76B [ 9 ]	7.62	0.10	1.00	0.00	0.30	0.08	64.31	75.14	82.15	$\checkmark$
	InternVL2.5-38B [ 8 ]	2.47	0.92	1.00	0.00	0.15	0.28	75.41	76.29	84.64	$\checkmark$
	InternVL2.5-78B [ 8 ]	0.22	8.03	0.50	0.67	0.05	0.44	74.44	77.73	77.17	✗
	InternVL3-38B [ 32 ]	2.81	0.67	1.00	0.00	0.15	0.24	78.52	80.03	87.80	$\checkmark$
	InternVL3-78B [ 32 ]	0.55	5.82	1.00	0.00	0.07	0.27	75.64	79.60	80.07	$\checkmark$
	Phi-4 [ 1 ]	0.97	3.83	1.00	0.00	0.13	0.04	71.97	75.32	79.61	$\checkmark$

Table 4: ANOVA and trend analysis across coverage levels (20%, 50%, 80%). Group 1–3 correspond to means at 20%, 50%, 80%.

Figure 4: An overview of GeoAoT. Given an input image, GeoAoT first leverages a pre-trained LMMs to generate an initial geo-guess. It then iteratively refines this estimate through AoT based multi-turn interactions, where the model reasons about uncertainty and issues actions to gather more evidence, operating purely at inference time without any additional training.

Method	Overall		Street			City			Country
	Dist. $\downarrow$	Score $\uparrow$	Acc. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$	Acc. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$	Acc. $\uparrow$	Rec. $\uparrow$	F1 $\uparrow$
LLaVA OneVision 72B	4081	53.68	0.112	0.097	0.104	0.241	0.206	0.222	0.508	0.482	0.494
(w/ AoT)	3007	61.20	0.150	0.130	0.139	0.304	0.261	0.281	0.579	0.544	0.561
$\Delta$ (AoT $-$ base)	-1074	7.527	$0.038$	$0.033$	$0.035$	$0.063$	$0.055$	$0.058$	0.071	0.062	0.067
Gemma3 27B	1685	72.76	0.207	0.178	0.191	0.426	0.366	0.393	0.685	0.681	0.683
(w/ AoT)	1475	74.95	0.228	0.196	0.211	0.442	0.380	0.409	0.711	0.704	0.708

Table 5: Performance of GeoAoT on the benchmark. Blue bold values represent the best AoT result. Red bold values represent the strongest improvement per column.

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