Placer mining is a mineral extraction method in floodplains that involves the removal of earth material to access mineral-laden sediments, a process that can have significant and long-term impacts on aquatic ecosystems. Given the widespread nature of mining, new tools are required to monitor the potential watershed-scale ecological impacts of placer mining. This study adapted and evaluated a deep learning model – a U-Net convolution neural network, and compared it to a traditional image classification method – random forests (RF) – to detect and quantify the area of post-placer mining disturbance at the watershed scale. Overall, both random forest and U-Net models performed well at classifying digitized image samples where placer disturbances were mapped. Sensitivity in placer classification was high, with both modelling frameworks achieving at least 75% accuracy in the classification of digitized placer samples in 7 out of 12 modelling scenarios. Misclassification of non-placer pixels as placer was highly variable among different models, data configurations, study sites, and time periods. Commission errors (i.e., incorrectly classifying a non-placer pixel as placer) were typically the result of models labelling water areas or forest areas as placer – errors which may have only marginal practical significance. In general, U-Net models performed better in terms of minimizing misclassification errors, whereas RF models performed slightly better in classifying known placer pixels. We conclude with discussions on the advantages of deploying U-Net and RF models for placer detection, challenges that may be encountered in operational systems that employ the models, and identifying outstanding issues which need to be addressed in future placer modelling studies.

砂矿开采是泛滥平原的一种矿物提取方法，涉及去除泥土材料以获取富含矿物质的沉积物，这一过程会对水生生态系统产生重大和长期的影响。鉴于采矿的广泛性，需要新的工具来监测砂矿开采对流域规模的潜在生态影响。本研究采用并评估了一个深度学习模型——U-Net 卷积神经网络，并将其与传统的图像分类方法——随机森林 (RF)——进行比较，以检测和量化流域尺度的砂矿后采矿干扰区域. 总体而言，随机森林和 U-Net 模型在对映射了砂矿扰动的数字化图像样本进行分类方面表现良好。砂矿分类灵敏度高，在 12 个建模场景中的 7 个中，这两个建模框架在数字化放置样本的分类中实现了至少 75% 的准确度。在不同的模型、数据配置、研究地点和时间段之间，将非砂矿像素错误分类为砂矿是高度可变的。委托错误（即，错误地将非砂矿像素分类为砂矿）通常是模型将水域或森林区域标记为砂矿的结果——这些误差可能只有边际的实际意义。一般来说，U-Net 模型在最小化错误分类错误方面表现更好，而 RF 模型在分类已知放置器像素方面表现稍好。我们最后讨论了部署 U-Net 和 RF 模型进行放置器检测的优势，