Operators of pipeline infrastructure buried underground are in many countries required to ensure that depth of cover—a measure of the quantity of soil covering a pipeline—lie within prescribed bounds. Traditionally, monitoring depth of cover at scale has been carried out qualitatively by means of visual inspection. We proceed instead to rely on airborne remote sensing techniques to obtain densely sampled ground surface point measurements from the pipeline's right of way, from which we determine depth of cover using automated algorithms. Proceeding in our manner presents a reproducible, quantitative approach to monitoring depth of cover, yet the demands thus made by the scale of real-world pipeline monitoring scenarios on compute and storage resources can be substantial. We show that the scalability afforded by the cloud can be leveraged to address such scenarios, distributing the algorithms we employ to take advantage of multiple compute nodes and exploiting elastic storage. While the use case underlying this paper is monitoring depth of cover, our proposed architecture can be applied more broadly to a wide variety of geospatial analytics tasks carried out 'in the large', including change detection, semantic classification or segmentation, or computation of vegetation indices.

We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.

We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of 'single-image' super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars.

The Sentinel-1 mission is a polar-orbiting satellite constellation for the continuation of C-band Synthetic Aperture Radar (SAR) applications. Contrary to its predecessor instruments onboard of ENVISAT and RADARSAT, the Sentinel-1 satellites will be operated following a predefined and fixed baseline acquisition scenario. This will significantly facilitate the development of fully automatic processing chains for the generation of higher-level geophysical products and their uptake in applications. This paper gives an overview of the potential use of Sentinel-1 for land applications, discussing different land cover products (permanent water bodies, forest/non-forest, rice) and parameters of high relevance for hydrological monitoring (soil moisture, snow and freeze/thaw status, surface inundation).

The realization that we see lines known to be parallel in space as lines that appear to converge in a corresponding vanishing point has led to techniques employed by artists since at least the Renaissance to render a credible impression of perspective. More recently, it has also led to techniques for recovering information embedded in images pertaining to the geometry of their underlying scene. In this paper, we explore the extraction of vanishing points in the aim of facilitating the reconstruction of Manhattan-world scenes. In departure from most vanishing point extraction methods, ours extracts a constellation of vanishing points corresponding, respectively, to the scene's two or three dominant pairwise-orthogonal orientations by integrating information across multiple views rather than from a single image alone. What makes a multiple-view approach attractive is that in addition to increasing robustness to segments that do not correspond to any of the three dominant orientations, robustness is also increased with respect to inaccuracies in the extracted segments themselves.