Shen Liang

and 2 more

One of the long-standing question in geoscience is whether there is a topographical signature of life. Recent development of space-borne LiDAR has led to massive data depicting planetary topographies, opening up unprecedented opportunities to make progress in answering this question. This is what we set out to do in an ongoing project named í µí±ƒí µí°´í µí±í µí°¾í µí°¸í µí±. A key step of PARKER is to find topograph-ical features that are potentially relevant to intrinsic differences between Earth and alien worlds. Due to the huge data volume, sequential feature extraction cannot meet our needs in PARKER. Hence, in this work we propose a GPU-accelerated framework for fast feature extraction of planetary LiDAR, which as far as we know is the first GPU-based solution for this task. Faced with multi-scale features and limited GPU memory, we present a novel pseudo-one-pass sweep (POPS) approach, leveraging memory-aware data grouping and incremental data transfer to address these challenges. We also develop a GPU-based solution to aggregate features extracted by POPS. Experiments on real and simulated data show that our algorithms are 2-3 orders of magnitude faster than their sequential counterparts and 1-2 orders of magnitude faster than MPI-based multi-core parallelism, enabling near real-time analytics of datasets with almost a billion points. Based on POPS, we have been able to efficiently evaluate the relevance of topographical features to intrinsic inter-planetary differences. So far, we have assessed the abilities of two feature extractions methods, PCA and STAT, to capture differences between Earth and Mars. Results show that PCA features on scales of 300-500m can best capture such differences. Thanks to the generic nature of POPS, we will be able to expand our studies to new feature extraction methods and other alien worlds than Mars in the next phase of PARKER.

Shen Liang

and 5 more

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.

Clement Perrin

and 11 more

Antoine Lucas

and 1 more

Mountainous landscape evolution under tropical and alpine environments is mainly dictated by climatic forcing which influences underlying mechanisms of geomorphic transport (e.g., soil formation, river dynamics, slope stability and mass wasting). The time scale over which this influence acts ranges from seasonal to decennial time span. On the seasonal time scale, for accessible locations and when manpower is available, direct observations and field survey are the most useful and standard approaches. While very limited studies have been focused on the the decennial and century scale due to observational constrains. Here, we present an open and reproducible pipeline based on historical aerial images (up to 70yrs time span) that includes sensor calibration, dense matching and elevation reconstruction over two areas of interest that represent pristine examples for tropical and alpine environments: The Rempart Canyon in Reunion Island, and the Bossons glacier in the French Alps share a limited accessibility (in time and space) that can be overcome only from remote-sensing. We reach unprecedented resolution: the aero-triangulation falls at sub-metric scale based on ground truth, which is comparable to the initial images spatial sampling. This provides elevation time series with a better resolution to most recent satellite images such as Pleiades. In the case of the Rempart Canyon, we identified and quantified the results of 2 landslides that occurred in 1965 and 2001, and characterized the landslides dynamics. As for the alpine case, we highlight the effect of the temperature plateau occurred during 1939-1970 in Europe before the well known accelerated retreat during the post-industrial period. In both cases, we emphasize the strong effect of extreme events over multi-decennial to century time-scales.