CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914532932190208 |
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| author | Ilyankou, Ilya Jongwiriyanurak, Natchapon Cheng, Tao Haworth, James |
| author_facet | Ilyankou, Ilya Jongwiriyanurak, Natchapon Cheng, Tao Haworth, James |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_12214 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images Ilyankou, Ilya Jongwiriyanurak, Natchapon Cheng, Tao Haworth, James Computer Vision and Pattern Recognition 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. |
| title | CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12214 |