CLIP the Landscape: Automated Tagging of Crowdsourced Landscape Images

Code

• github.com/SpaceTimeLab/ClipTheLandscape
  OfficialIn papernone★ 5

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 competitionhttps://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 pipelinehttps://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.

Reproductions