Spatially explicit information on land cover (LC) is commonly derived using remote sensing, but the lack of training data still remains a major challenge for producing accurate LC products. Here, we develop a computer vision methodology to extract LC information from photos from the Land Use-Land Cover Area Frame Survey (LUCAS). Given the large number of photographs available and the comprehensive spatial coverage, the objective is to show how the automatic classification of photos could be used to develop reference data sets for training and validation of LC products as well as other purposes. We first selected a representative sample of 1120 photos covering eight major LC types across the European Union. We then applied semantic segmentation to these photos using a neural network (Deeplabv3+) trained with the ADE20k dataset. For each photo, we extracted the original LC identified by the LUCAS surveyor, the segmented objects, and the pixel count for each ADE20k class. Using the latter as input features, we then trained a Random Forest model to classify the LC of the photo. Examining the relationship between the objects/features extracted by Deeplabv3+ and the LC labels provided by the LUCAS surveyors demonstrated how the LC classes can be decomposed into multiple objects, highlighting the complexity of LC classification from photographs. The results of the classification show a mean F1 Score of 89%, increasing to 93% when the Wetland class is not considered. Based on these results, this approach holds promise for the automated retrieval of LC information from the rich source of LUCAS photographs as well as the increasing number of geo-referenced photos now becoming available through social media and sites like Mapillary or Google Street View.

土地覆盖 (LC) 的空间明确信息通常通过遥感获得，但缺乏训练数据仍然是生产准确的 LC 产品的主要挑战。在这里，我们开发了一种计算机视觉方法，从土地利用-土地覆盖面积框架调查 (LUCAS) 的照片中提取 LC 信息。鉴于可用照片数量巨大且空间覆盖全面，目标是展示如何使用照片自动分类来开发参考数据集，用于 LC 产品的培训和验证以及其他目的。我们首先选择了 1120 张照片的代表性样本，涵盖欧盟八种主要 LC 类型。然后，我们使用使用 ADE20k 数据集训练的神经网络 (Deeplabv3+) 对这些照片应用语义分割。对于每张照片，我们提取了 LUCAS 测量员识别的原始 LC、分割的对象以及每个 ADE20k 类别的像素计数。然后，我们使用后者作为输入特征，训练随机森林模型来对照片的 LC 进行分类。检查 Deeplabv3+ 提取的对象/特征与 LUCAS 测量员提供的 LC 标签之间的关系，演示了如何将 LC 类别分解为多个对象，突出了从照片中进行 LC 分类的复杂性。分类结果显示平均 F1 分数为 89%，如果不考虑湿地类别，则增加到 93%。基于这些结果，这种方法有望从丰富的 LUCAS 照片来源以及现在通过社交媒体和 Mapillary 或 Google Street View 等网站获得的越来越多的地理参考照片中自动检索 LC 信息。