  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.
