Deep learning methods applied to digital elevation models: state of the art

Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.Functional Quality of Digital Elevation Models in Engineering’ of the State Agency Research of SpainPID2019-106195RB- I00/AEI/10.13039/50110001103

Automated Impact Crater Detection and Characterization Using Digital Elevation Data

Impact craters are used as subjects for the remote study of a wide variety of surface and subsurface processes throughout the solar system. Their populations and shape characteristics are collected, often manually, and analysed by a large community of planetary scientists. This research investigates the application of automated methods for both the detection and characterization of impact craters on the Moon and Mars, using machine learning techniques and digital elevation data collected by orbital spacecraft. We begin by first assessing the effect of lunar terrain type variation on automated crater detection results. Next, we develop a novel automated crater degradation classification system for martian complex craters using polynomial profile approximation. This work identifies that surface age estimations and crater statistics acquired through automatic crater detection are influenced by terrain type, with unique detection error responses. Additionally, we demonstrate an objective system that can be used to automate the classification of crater degradation states, and identify some potential areas of improvement for such a system

Improving filtering methods based on the Fast Fourier Transform to delineate objective relief domains: An application to Mare Ingenii lunar area

A recent study has proven that high-pass filtering (HPF) based on the Fast Fourier Transform (FFT) is a rapid and efficient computational method for the semi-automated detection of geomorphic features from high-resolution digital elevation models (DEM). Although this new approach shows great potential for cartographic purposes using remote sensing data, some methodological improvements are still required in the following areas: (i) to develop a robust criteria for filter radius selection; (ii) to test the relationship between filter vectors and landscape form, and explore how DEM artefacts (vegetation, anthropic structures, etc.) can interfere with landform detection; and (iii) to explore filter response regarding generalisation and blurring effects when working with landscapes composed of landforms of different scales that are superimposed on one another. These topics are addressed here through two experiments (Experiment_1 and Experiment_2) with synthetic digital relief models inspired in the lunar landscape. Finally, the improved methodology was applied on the Mare Ingenii lunar relief (Experiment_3) using the Lunar Orbiter Laser Altimeter DEM and the results were tested against ground truths (GTs) developed using the extensive database available at Astropedia website and an ad hoc crater map. The analysis of existing frequencies in a 2D DEM signal through the true magnitude-true frequency plot provides an objective method for filter radius selection, and the use of a Butterworth transference function enables a more versatile filtering. Experiment_1 demonstrates a close correspondence between vectors obtained by filtering called Filtered Geomorphic References (FGRs) and the synthetic landform selected. The accuracy indicators from Experiment_1 and 2 show the good results obtained in the correspondence between FGRs and crater depressions, either from flat-bottomed to bowl shapes. Experiments 2 and 3 confirm that in landscapes generated by superimposed geomorphic features of different sizes, the smaller the crater, the better the filters detect its boundaries. Moreover, the spatial repeatability of FGRs can be used as a cartographic criterion in the identification of crater shape depressions or hills. Besides, the criterion is useful to assess true reality mapped in the GT employed. Finally, the objective geomorphic units obtained by combining the FGRs demonstrate their usefulness for the objective characterisation of the moonscape. Using the synthetic landscapes, the FGRs identify those relief domains composed of depressions and hills.This work was carried out as part of the Projects: 29.P114.64004 (UC); 29.P203.64004 (UC); RECORNISA (FLTQ-UC)

Doppler Lidar Vector Retrievals and Atmospheric Data Visualization in Mixed/Augmented Reality

abstract: Environmental remote sensing has seen rapid growth in the recent years and Doppler wind lidars have gained popularity primarily due to their non-intrusive, high spatial and temporal measurement capabilities. While lidar applications early on, relied on the radial velocity measurements alone, most of the practical applications in wind farm control and short term wind prediction require knowledge of the vector wind field. Over the past couple of years, multiple works on lidars have explored three primary methods of retrieving wind vectors viz., using homogeneous windfield assumption, computationally extensive variational methods and the use of multiple Doppler lidars. Building on prior research, the current three-part study, first demonstrates the capabilities of single and dual Doppler lidar retrievals in capturing downslope windstorm-type flows occurring at Arizona’s Barringer Meteor Crater as a part of the METCRAX II field experiment. Next, to address the need for a reliable and computationally efficient vector retrieval for adaptive wind farm control applications, a novel 2D vector retrieval based on a variational formulation was developed and applied on lidar scans from an offshore wind farm and validated with data from a cup and vane anemometer installed on a nearby research platform. Finally, a novel data visualization technique using Mixed Reality (MR)/ Augmented Reality (AR) technology is presented to visualize data from atmospheric sensors. MR is an environment in which the user's visual perception of the real world is enhanced with live, interactive, computer generated sensory input (in this case, data from atmospheric sensors like Doppler lidars). A methodology using modern game development platforms is presented and demonstrated with lidar retrieved wind fields. In the current study, the possibility of using this technology to visualize data from atmospheric sensors in mixed reality is explored and demonstrated with lidar retrieved wind fields as well as a few earth science datasets for education and outreach activities.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201

Probabilistic Surface Characterization for Safe Landing Hazard Detection and Avoidance (HDA)

Apparatuses, systems, computer programs and methods for performing hazard detection and avoidance for landing vehicles are provided. Hazard assessment takes into consideration the geometry of the lander. Safety probabilities are computed for a plurality of pixels in a digital elevation map. The safety probabilities are combined for pixels associated with one or more aim points and orientations. A worst case probability value is assigned to each of the one or more aim points and orientations

On Multi-Resolution 3D Orbital Imagery and Visualisation for Mars Geological Analysis

Mars Science Laboratory has revealed a dynamic history of water as the rover has ascended the mysterious Mount Sharp in Gale crater. Because rovers only “see” their local environment, planetary scientists rely on satellite-based orbital imagery to understand the regional geology of Gale crater. However, orbital imagery is map-view—viewed from above, lacking perspective—which presents challenges to interpretation of stratigraphy. 3D visualisation is an emerging opportunity to study orbital images in more intuitive, field-like environments, but has had limited application to Mars. In this work, I formulate and analyse 3D orbital imagery over Gale crater, Mars to investigate the stratigraphy of Mount Sharp 700 m above and 40 km away from MSL. First, I process orbital imagery from the HRSC, CTX, and HiRISE cameras into 3D digital terrain models (DTMs). I then co-register and evaluate these DTMs using statistical tools and existing products to build a new, validated, multi-resolution basemap tied down to MOLA. Sakarya Vallis, a 400-m deep canyon on Mount Sharp, was then analysed in a 3D environment at 1 m/px. From measurements of exposed rock layers, I construct cross-sections, stratigraphic logs, and a geological unit map to capture this geology. Seven geological units are interpreted across 1 km of exposure, varying in thicknesses (10–174 m) and dips (3–12º). These units may reveal a cyclic depositional environment; a progradational sequence and channel; and unconformities. This work therefore suggests two periods of sub-aqueous deposition in this region during the Late Noachian to Early Hesperian. These results further provide geological context of Gale crater as MSL ascends Mount Sharp, and future inputs for palaeoenvironmental models of Gale crater

Analysis and design of a capsule landing system and surface vehicle control system for Mars exploration

Problems related to an unmanned exploration of the planet Mars by means of an autonomous roving planetary vehicle are investigated. These problems include: design, construction and evaluation of the vehicle itself and its control and operating systems. More specifically, vehicle configuration, dynamics, control, propulsion, hazard detection systems, terrain sensing and modelling, obstacle detection concepts, path selection, decision-making systems, and chemical analyses of samples are studied. Emphasis is placed on development of a vehicle capable of gathering specimens and data for an Augmented Viking Mission or to provide the basis for a Sample Return Mission