Computer vision model for detecting block falls at the martian north polar region.

Abstract

<p>Dynamic changes of Martian north polar scarps present a valuable insight into the planet's natural climate cycles (Byrne, 2009; Head et al., 2003)<sup>1,2</sup>. Annual avalanches and block falls are amongst the most noticeable surface processes that can be directly linked with the extent of the latter dynamics (Fanara et al, 2020)<sup>3</sup>. New remote sensing approaches based on machine learning allow us to make precise records of the aforementioned mass wasting activity by automatically extracting and analyzing bulk information obtained from satellite imagery.  Previous studies have concluded that a Support Vector Machine (SVM) classifier trained using Histograms of Oriented Gradients (HOG) can be used to efficiently detect block falls, even against backgrounds with increased complexity (Fanara et al., 2020)<sup>4</sup>. We hypothesise that this pretrained model can now be utilized to generate an extended dataset of labelled image data, sufficient in size to opt for a deep learning approach. On top of improving the detection model we also attempt to address the image co-registration protocol. Prior research has suggested this to be a substantial bottleneck, which reduces the amounts of suitable images. We plan to overcome these limitations either by extending our model to include multi-sensor data, or by deploying improved methods designed for exclusively optical data (e.g.  COSI-CORR software (Ayoub, Leprince and Avouac, 2017)<sup>5</sup>).  The resulting algorithm should be a robust solution capable of improving on the already established baselines of 75.1% and 8.5% for TPR and FDR respectively (Fanara et al., 2020)4. The NPLD is our primary area of interest due to it’s high levels of activity and good satellite image density, yet we also plan to apply our pipeline to different surface changes and Martian regions as well as on other celestial objects.</p><p> </p><p>1. Head, J.W., Mustard, J.F., Kreslavsky, M.A., Milliken, R.E., Marchant, D.R., 2003. Recent ice ages on Mars. Nature 426, 797–802</p><p>2. Byrne, S., 2009. The polar deposits of Mars. Annu. Rev. Earth Planet. Sci. 37, 535–560.</p><p>3. Fanara, K. Gwinner, E. Hauber, J. Oberst, Present-day erosion rate of north polar scarps on Mars due to active mass wasting; Icarus,Volume 342, 2020; 113434, ISSN 0019-1035.</p><p>4. Fanara, K. Gwinner, E. Hauber, J. Oberst, Automated detection of block falls in the north polar region of Mars; Planetary and Space Science, Volume 180, 2020; 104733, ISSN 0032-0633.</p><p>5. Ayoub, F.; Leprince, S.; Avouac, J.-P. User’s Guide to COSI-CORR Co-registration of Optically Sensed Images and Correlation; California Institute of Technology: Pasadena, CA, USA, 2009; pp. 1–49.</p>

Detecting the Sources of Ice Block Falls at the Martian North Polar Scarps by Analysis of Multi-Temporal Hirise Imagery

“…We will also probe into the flux and volume estimation for ongoing mass wasting. Our work and the research of Martynchuk et al (2021) on block fall detection will be complementary with the common goal of monitoring the scarps and comparing the temporal and spatial variation of erosion to better understand the related driving factors.…”

“…Fanara et al (2020a,b) have detected large numbers of recently fallen ice blocks at the foot of scarps by using machine learning and estimated a scarp retreat rate of ∼0.2 m/kyr. Herkenhoff et al (2020) have also obtained the volume of ice block falls through manually detecting newly appeared blocks at the foot of the scarps and found a similar rate as Fanara et al (2020b), while Martynchuk et al (2021) is using deep * Corresponding author learning to extend the work of Fanara et al (2020b) and map all ice block falls of the whole north polar region through time.…”