FARMYARD: A Generic GPU-based Pipeline for Feature Discovery from
Massive Planetary LiDAR Data
Abstract
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.