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.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.

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This article examines the decline in animal experimentation observed in the last 25 years. It considers possible reasons for this and its significance. However, it also asks whether more could have been achieved, and considers what needs to be done to ensure that the current rate of decline is continued and increased — not just in the UK, but also in Europe and beyond. It is concluded that future reductions in animal experimentation need to be a matter of policy, rather than the product of circumstance. Furthermore, it is concluded that policy objectives alone will be insufficient unless supported by a clear strategy and adequate resources for effective implementation.