论文标题
自动绿绿色：地理空间机器学习的自动标签生成
AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning
论文作者
论文摘要
监督学习的主要挑战是人类标记的数据的可用性。我们评估了一个大数据处理管道，以自动生成标签以获取遥感数据。它基于从诸如例如激光痛测量。使用栅格统计层的简单组合，可以证明可以以〜0.9的精度生成多个类。作为概念验证，我们利用大型地理数据平台IBM对在具有多个土地覆盖类别的密集城市地区动态生成此类标签。此处提出的一般方法是独立的，它可以适应其他卫星模式的标签，以便在架空图像上进行机器学习以进行土地使用分类和对象检测。
A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations of the rasterized statistical layers, it is demonstrated that multiple classes can be generated at accuracies of ~0.9. As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes. The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification and object detection.
