Land cover maps are key elements for understanding global climate and land use. They are often created by automatically classifying satellite imagery. However, inconsistencies in classification may be introduced inadvertently. Experts can reconcile classification discrepancies by viewing satellite and high-resolution images taken on the ground.

We present and evaluate a framework to filter relevant geo-tagged photos from social network sites for land cover classification tasks. Social network sites offer massive amounts of potentially relevant data, but its quality and fitness for research purposes must be verified.

Our framework uses computer vision to analyse the content of geo-tagged photos on social network sites to generate descriptive tags. These are used to train artificial neural networks to predict a photo’s relevance for land cover classification. We apply our models to four African case studies and their neighbours. The framework has been implemented within Geo-Wiki to fetch relevant photos from Flickr.

土地覆盖图是了解全球气候和土地利用的关键要素。它们通常是通过自动分类卫星图像来创建的。但是，分类中的不一致可能会无意中引入。专家可以通过查看地面上拍摄的卫星图像和高分辨率图像来调和分类差异。

我们提出并评估了一个框架，该框架可以过滤来自社交网站的相关带有地理标签的照片，以进行土地覆盖分类任务。社交网站提供了大量潜在的相关数据，但是必须验证其质量和适合研究目的。

我们的框架使用计算机视觉来分析社交网站上带有地理标签的照片的内容，以生成描述性标签。这些用于训练人工神经网络，以预测照片与土地覆被分类的相关性。我们将模型应用于四个非洲案例研究及其邻国。该框架已在Geo-Wiki中实现，以从Flickr中获取相关照片。