Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework

Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework | Signal Canvas | ScienceToStartup

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Canonical route: /signal-canvas/global-cross-modal-geo-localization-a-million-scale-dataset-and-a-physical-consistency-learning-framework

stale

Proof freshness: stale
Proof status: unverified
Display score: 8/10
Last proof check: 2026-03-19
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID global-cross-modal-geo-localization-a-million-scale-dataset-and-a-physical-consistency-learning-framework | Route /signal-canvas/global-cross-modal-geo-localization-a-million-scale-dataset-and-a-physical-consistency-learning-framework

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Evidence Receipt

Route status: building

Claims: 8

References: Pending verification

Proof: Verification pending

Freshness state: computing

Source paper: Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework

PDF: https://arxiv.org/pdf/2603.08491v1

Source count: Pending verification

Coverage: 33%

Last proof check: 2026-03-19T18:48:05.835Z

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Watch and verify: Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework

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Subject: Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
we introduce CORE, the first million-scale dataset dedicated to global CMGL
Implicationpartial
Explicitly stated in the abstract with specific dataset size and scope
Verificationpartial
partial
Evidencepartial
CORE comprises 1,034,786 cross-view images sampled from 225 distinct geographic regions across all continents
Implicationpartial
Specific numeric data provided in the abstract
Verificationpartial
partial
Evidencepartial
existing researches are constrained by narrow geographic coverage and simplistic scene diversity
Implicationpartial
Directly stated as a limitation of prior work in the abstract
Verificationpartial
partial
Evidencepartial
PLANet significantly outperforms state-of-the-art methods
Implicationpartial
Directly stated in abstract with reference to extensive experiments
Verificationpartial
partial
Evidencepartial
We leverage the zero-shot reasoning of Large Vision-Language Models (LVLMs) to synthesize high-quality scene descriptions rich in discriminative cues
Implicationpartial
Explicitly stated methodology in the abstract
Verificationpartial
partial
Evidencepartial
PLANET introduces a novel contrastive learning paradigm to guide textual representations in capturing the intrinsic physical signatures of satellite imagery
Implicationpartial
Direct description of the method's technical innovation
Verificationpartial
partial
Evidencepartial
which is crucial for pedestrian navigation and emergency response
Implicationpartial
Direct statement about the importance and market applications
Verificationpartial
partial
Evidencepartial
offering an unprecedented variety of perspectives in varying environmental conditions and urban layouts
Implicationpartial
Direct claim about dataset characteristics with supporting context
Verificationpartial
partial

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Global Cross-Modal Geo-Localization: A Million-Scale Dataset and a Physical Consistency Learning Framework

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