论文标题
从LiDAR数据中学习和识别考古特征
Learning and Recognizing Archeological Features from LiDAR Data
论文作者
论文摘要
我们提出了一条遥感管道，该管道通过机器和深度学习来处理LiDAR（光检测和范围）数据，以在大地理空间数据平台上应用考古特征检测，例如IBM对地球镜。 如今，考古学家对视觉调查大量（RAW）LIDAR数据的任务感到不知所措，以确定感兴趣的领域以进行现场检查。我们展示了一条软件系统管道，该管道在专家生产力方面可节省大量，而仅缺少一小部分工件。 我们的工作与基于领域知识的有效空间分割程序一起采用了人工神经网络。由于植被覆盖物和古代结构的腐烂，数据处理受到有限数量的训练标签和嘈杂的激光雷达信号的限制。我们旨在以监督的方式来识别具有考古文物的地理空间区域，从而使域专家可以根据她的需求灵活调整参数。
We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope. Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts. Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs.
