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GeoSEAN model offers explainable country-level image geolocation for ASEAN regions

By PulseAugur Editorial · [2 sources] ·

Researchers have developed GeoSEAN, a novel system for country-level image geolocation within ASEAN regions, addressing the challenge of similar visual characteristics across borders. The system utilizes a multilayer perceptron (MLP) classifier, achieving an 85.91% accuracy and F1 score on a dataset of 4,850 images. GeoSEAN enhances explainability by employing CLIP attention rollout, YOLOv2 object detection, and Energy Based Pointing Game metrics to analyze the visual cues behind its predictions, revealing that object frequency does not always correlate with attention density. AI

IMPACT This research advances explainable AI techniques in computer vision for geographic localization tasks.

RANK_REASON The cluster contains an academic paper detailing a new model and methodology.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

GeoSEAN model offers explainable country-level image geolocation for ASEAN regions

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COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Muhamad Syukron, Danish Rafie Ekaputra, Tintrim Dwi Ary Widhianingsih ·

    GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions

    arXiv:2607.12284v1 Announce Type: new Abstract: Image geolocation aims to infer the geographic origin of an image from visual content alone. However, this task remains challenging in regions where countries share similar urban, roadside, architectural, and environmental character…

  2. arXiv cs.CV TIER_1 English(EN) · Tintrim Dwi Ary Widhianingsih ·

    GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions

    Image geolocation aims to infer the geographic origin of an image from visual content alone. However, this task remains challenging in regions where countries share similar urban, roadside, architectural, and environmental characteristics. Many existing geolocation models focus o…

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