CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images

• URL: http://arxiv.org/abs/2506.12214v1
• Date: Fri, 13 Jun 2025 20:32:58 GMT
• Title: CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images
• Authors: Ilya Ilyankou, Natchapon Jongwiriyanurak, Tao Cheng, James Haworth,
• Abstract summary: We present a CLIP-based, multi-modal, multi-label classifier for predicting geographical context tags from landscape photos.<n>We show that combining location and title embeddings with image features improves accuracy over using image embeddings alone.
• Score: 0.5999777817331317
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present a CLIP-based, multi-modal, multi-label classifier for predicting geographical context tags from landscape photos in the Geograph dataset--a crowdsourced image archive spanning the British Isles, including remote regions lacking POIs and street-level imagery. Our approach addresses a Kaggle competition\footnote{https://www.kaggle.com/competitions/predict-geographic-context-from-landscape-photos} task based on a subset of Geograph's 8M images, with strict evaluation: exact match accuracy is required across 49 possible tags. We show that combining location and title embeddings with image features improves accuracy over using image embeddings alone. We release a lightweight pipeline\footnote{https://github.com/SpaceTimeLab/ClipTheLandscape} that trains on a modest laptop, using pre-trained CLIP image and text embeddings and a simple classification head. Predicted tags can support downstream tasks such as building location embedders for GeoAI applications, enriching spatial understanding in data-sparse regions.

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