Radar mapping of Isunnguata Sermia, Greenland

This is the published version. Copyright 2013 International Glaciological SocietyIce thickness estimates using advanced nadir sounding and tomographic radar processing techniques are compared and combined in a study of Isunnguata Sermia glacier, Greenland. Using an ensemble of Operation IceBridge flight lines spaced at 500 m intervals and running approximately along the flow direction, we find there is a statistically excellent comparison between subglacial terrains derived from two-dimensional tomography and gridded nadir sounding. Analysis shows that tomographic data better capture short wavelength (1–2 km) patterns in basal terrain, but interpolated nadir sounding data yield more spatially extensive and continuous coverage across the glacier, especially in deep subglacial troughs. Using derived surface and basal topography maps, we find that driving stress and measured and modeled surface velocity comparisons indicate that basal sliding is an important component of the glacier motion, but is also only weakly coupled to the detailed bed topography save for the deepest troughs. As might be expected for this land-terminating, relatively slow-moving glacier, the subglacial and proglacial topography is similar, suggesting the erosional processes acting on the modern glacier bed once helped sculpt the now exposed land

Radiometer offsets and count conversion coefficients for the Earth Radiation Budget Experiment (ERBE) spacecraft for the years 1984, 1985, and 1986

A compendium is presented of the ground and inflight scanner and nonscanner offsets and count conversion (gain) coefficients used for the Earth Radiation Budget Experiment (ERBE) production processing of data from the ERBS, NOAA-9, and NOAA-10 satellites for the 1 Nov. 1984 to 31 Dec. 1986

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

AIBA: An AI Model for Behavior Arbitration in Autonomous Driving

Driving in dynamically changing traffic is a highly challenging task for autonomous vehicles, especially in crowded urban roadways. The Artificial Intelligence (AI) system of a driverless car must be able to arbitrate between different driving strategies in order to properly plan the car's path, based on an understandable traffic scene model. In this paper, an AI behavior arbitration algorithm for Autonomous Driving (AD) is proposed. The method, coined AIBA (AI Behavior Arbitration), has been developed in two stages: (i) human driving scene description and understanding and (ii) formal modelling. The description of the scene is achieved by mimicking a human cognition model, while the modelling part is based on a formal representation which approximates the human driver understanding process. The advantage of the formal representation is that the functional safety of the system can be analytically inferred. The performance of the algorithm has been evaluated in Virtual Test Drive (VTD), a comprehensive traffic simulator, and in GridSim, a vehicle kinematics engine for prototypes.Comment: 12 page

Multi-Task Spatiotemporal Neural Networks for Structured Surface Reconstruction

Deep learning methods have surpassed the performance of traditional techniques on a wide range of problems in computer vision, but nearly all of this work has studied consumer photos, where precisely correct output is often not critical. It is less clear how well these techniques may apply on structured prediction problems where fine-grained output with high precision is required, such as in scientific imaging domains. Here we consider the problem of segmenting echogram radar data collected from the polar ice sheets, which is challenging because segmentation boundaries are often very weak and there is a high degree of noise. We propose a multi-task spatiotemporal neural network that combines 3D ConvNets and Recurrent Neural Networks (RNNs) to estimate ice surface boundaries from sequences of tomographic radar images. We show that our model outperforms the state-of-the-art on this problem by (1) avoiding the need for hand-tuned parameters, (2) extracting multiple surfaces (ice-air and ice-bed) simultaneously, (3) requiring less non-visual metadata, and (4) being about 6 times faster.Comment: 10 pages, 7 figures, published in WACV 201

Road layout understanding by generative adversarial inpainting

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in. The ability to discern static environment and dynamic entities provides a comprehension of the road layout that poses constraints to the reasoning process about moving objects. We pursue this through a GAN-based semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings. We evaluate this task on the Cityscapes dataset and on a novel synthetically generated dataset obtained with the CARLA simulator and specifically designed to quantitatively evaluate semantic segmentation inpaintings. We compare our methods with a variety of baselines working both in the RGB and segmentation domains

The trough-system algorithm and its application to spatial modeling of Greenland subglacial topography

This is the published version. Copyright 2014 Herzfeld et al.Dynamic ice-sheet models are used to assess the contribution of mass loss from the Greenland ice sheet to sea-level rise. Mass transfer from ice sheet to ocean is in a large part through outlet glaciers. Bed topography plays an important role in ice dynamics, since the acceleration from the slow-moving inland ice to an ice stream is in many cases caused by the existence of a subglacial trough or trough system. Problems are that most subglacial troughs are features of a scale not resolved in most ice-sheet models and that radar measurements of subglacial topography do not always reach the bottoms of narrow troughs. The trough-system algorithm introduced here employs mathematical morphology and algebraic topology to correctly represent subscale features in a topographic generalization, so the effects of troughs on ice flow are retained in ice-dynamic models. The algorithm is applied to derive a spatial elevation model of Greenland subglacial topography, integrating recently collected radar measurements (CReSIS data) of the Jakobshavn Isbræ, Helheim, Kangerdlussuaq and Petermann glacier regions. The resultant JakHelKanPet digital elevation model has been applied in dynamic ice-sheet modeling and sea-level-rise assessment

Radiometer offsets and count conversion coefficients for the Earth Radiation Budget Experiment (ERBE) spacecraft for the years 1987, 1988, and 1989

This document contains a compendium of the ground and in-flight scanner and non-scanner offsets and count conversion (gain) coefficients used for the Earth Radiation Budget Experiment (ERBE) production processing of data from the ERBS satellite for the period from 1 January 1987 to 31 December 1989; for the NOAA-9 satellite, for the month of January 1987; and for the NOAA-10 satellite, for the period from 1 January 1987 to 31 May 1989

Three-dimensional topology dataset of folded radar stratigraphy in northern Greenland

We present a dataset of reconstructed three-dimensional (3D) englacial stratigraphic horizons in northern Greenland. The data cover four different regions representing key ice-dynamic settings in Greenland: (i) the onset of Petermann Glacier, (ii) a region upstream of the 79° North Glacier (Nioghalvfjerdsbræ), near the northern Greenland ice divide, (iii) the onset of the Northeast Greenland Ice Stream (NEGIS) and (iv) a 700 km wide region extending across the central ice divide over the entire northern part of central Greenland. In this paper, we promote the advantages of a 3D perspective of deformed englacial stratigraphy and explain how 3D horizons provide an improved basis for interpreting and reconstructing the ice-dynamic history. The 3D horizons are provided in various formats to allow a wide range of applications and reproducibility of results