In recent decades, with the placement of LiDAR remote sensing instruments in orbit, we now have global coverage of the bare-ground elevation on the Earth, Mars and beyond. Encoded in such planetary LiDAR data are interesting landscape features that promise to further our knowledge of planetary topography. However, discovery of such features entails 3 major challenges: 1) massive data; 2) the need for local multi-scale features; 3) sensitivity to interfering factors. To address these challenges, we propose FARMYARD, a generic pipeline for \underline{F}e\underline{a}ture Discove\underline{r}y Fro\underline{m} Planetar\underline{y} LiD\underline{AR} \underline{D}ata Data. To our knowledge, this is the first time such a pipeline has been proposed, which provides a brand new methodology for comparative studies of planetary topography. Specifically, drawing on the parallel computing power of the Graphics Processing Unit (GPU), we propose a novel pseudo-on-pass sweep (POPS) framework for fast and memory-efficient feature extraction for massive planetary LiDAR data, a two-level division scheme for local regions with support for multi-scale features, and a Domain-Shifted Partition (DSP) scheme for feature evaluation that is robust against interfering factors. To showcase the utility of our FARMYARD pipeline, we deploy it to a real-world research project, which seeks to find topographical signatures of life by discovering features that can potentially distinguish between the Earth and alien worlds with no known life activity. We also highlight the efficiency of our POPS framework with experiments on both synthetic and real data, which can be thousands of times faster than its CPU-based counterpart, including a multi-core parallel solution.

Observational records of rapidly varying magnetic fields strongly constrain our understanding of core flow dynamics and Earth’s dynamo. Archeomagnetic analyses of densely sampled artefacts from the Near-East have suggested that the intensity variation during the first millennium BC was punctuated with two geomagnetic spikes with rates of change of intensity exceeding 1 μT/y, whose extreme behaviour is challenging to explain from a geodynamo perspective. By applying a new transdimensional Bayesian method designed to capture variations on both long and short timescales, we show that the data considered only at the fragment (thermal-unit) level require a complex intensity variation with six spikes, each with a duration between ~30-100 years. However, the nature of the inferred intensity evolution and the number of spikes detected are fragile and highly dependent on the specific treatment of the archeomagnetic data. No spikes are observed when the data are considered only at the level of a group of fragments from the same archeological context, with a minimum of three different artefacts per context. Furthermore, the number of spikes decreases to zero when increasing the error budget for the intensity within reasonable levels of 3-6 μT and the data age uncertainty up to 50 years. Thus, depending on the choices made, the Near-Eastern data are compatible with a broad range of time-dependence, from six spikes at one extreme to zero spikes on the other, the latter associated with much more modest rates of change of ~0.2-0.3 μT/y, comparable to secular variation at other periods and in other regions.