SUBMANIFOLD SPARSE CONVOLUTIONAL NETWORKS FOR SEMANTIC SEGMENTATION OF LARGE-SCALE ALS POINT CLOUDS

Semantic segmentation of point clouds is one of the main steps in automated processing of data from Airborne Laser Scanning (ALS). Established methods usually require expensive calculation of handcrafted, point-wise features. In contrast, Convolutional Neural Networks (CNNs) have been established as powerful classifiers, which at the same time also learn a set of features by themselves. However, their application to ALS data is not trivial. Pure 3D CNNs require a lot of memory and computing time, therefore most related approaches project ALS point clouds into two-dimensional images. Sparse Submanifold Convolutional Networks (SSCNs) address this issue by exploiting the sparsity often inherent in 3D data. In this work, we propose the application of SSCNs for efficient semantic segmentation of voxelized ALS point clouds in an end-to-end encoder-decoder architecture. We evaluate this method on the ISPRS Vaihingen 3D Semantic Labeling benchmark and achieve state-of-the-art 85.0% overall accuracy. Furthermore, we demonstrate its capabilities regarding large-scale ALS data by classifying a 2.5 km2 subset containing 41 M points from the Actueel Hoogtebestand Nederland (AHN3) with 95% overall accuracy in just 48 s inference time or with 96% in 108 s

Accurate matching and reconstruction of line features from ultra high resolution stereo aerial images

In this study, a new reconstruction approach is proposed for the line segments that are nearly-aligned(<= 10 degrees) with the epipolar line. The method manipulates the redundancy inherent in line pair-relations to generate artificial 3D point entities and utilize those entities during the estimation process to improve the height values of the reconstructed line segments. The best point entities for the reconstruction are selected based on a newly proposed weight function. To test the performance of the proposed approach, we selected three test patches over a built up area of the city of Vaihingen-Germany. Based on the results, the proposed approach produced highly promising reconstruction results for the line segments that are nearly-aligned with the epipolar line

Modeling spacecraft oscillations in hrsc images of mars express

Since January 2004 the High Resolution Stereo Camera (HRSC) is mapping planet Mars. The multi-line sensor on board the ESA Mission Mars Express images the Martian surface with a resolution of up to 1 2 m per pixel in three dimensions and in color. As part of the Photogrammetric/Cartographic Working Group of the HRSC Science Team the Institute of Photogrammetry and GeoInformation (IPI) of the Leibniz Universitat Hannover is involved in photogrammetrically processing the HRSC image data. To derive high quality 3D surface models, color orthoimages or other products, the accuracy of the observed position and attitude information in many cases should be improved. This is carried out via a bundle adjustment. In a considerable number of orbits the results of the bundle adjustment are disturbed by high frequency oscillations. This paper describes the impact of the high frequency angular spacecraft movement to the processing results of the last seven years of image acquisition and how the quality of the HRSC data products is significantly improved by modeling these oscillations.DLR/50 QM 090

Intercomparison of planetary boundary layer heights using remote sensing retrievals and ERA5 reanalysis over Central Amazonia

The atmospheric boundary layer height (zi) is a key parameter in the vertical transport of mass, energy, moisture, and chemical species between the surface and the free atmosphere. There is a lack of long-term and continuous observations of zi, however, particularly for remote regions, such as the Amazon forest. Reanalysis products, such as ERA5, can fill this gap by providing temporally and spatially resolved information on zi. In this work, we evaluate the ERA5 estimates of zi (zi-ERA5) for two locations in the Amazon and corrected them by means of ceilometer, radiosondes, and SODAR measurements (zi-experimental). The experimental data were obtained at the remote Amazon Tall Tower Observatory (ATTO) with its pristine tropical forest cover and the T3 site downwind of the city of Manaus with a mixture of forest (63%), pasture (17%), and rivers (20%). We focus on the rather typical year 2014 and the El Ni&ntilde;o year 2015. The comparison of the experimental vs. ERA5 zi data yielded the following results: (i) zi-ERA5 underestimates zi-experimental daytime at the T3 site for both years 2014 (30%, underestimate) and 2015 (15%, underestimate); (ii) zi-ERA5 overestimates zi-experimental daytime at ATTO site (12%, overestimate); (iii) during nighttime, no significant correlation between the zi-experimental and zi-ERA5 was observed. Based on these findings, we propose a correction for the daytime zi-ERA5, for both sites and for both years, which yields a better agreement between experimental and ERA5 data. These results and corrections are relevant for studies at ATTO and the T3 site and can likely also be applied at further locations in the Amazon

Scalar turbulent behavior in the roughness sublayer of an Amazonian forest.

An important current problem in micrometeorology is the characterization of turbulence in the roughness sublayer (RSL), where most of the measurements above tall forests are made. There, scalar turbulent fluctuations display significant departures from the predictions of Monin?Obukhov similarity theory (MOST). In this work, we analyze turbulence data of virtual temperature, carbon dioxide, and water vapor in the RSL above an Amazonian forest (with a canopy height of 40&#8239;m), measured at 39.4 and 81.6&#8239;m above the ground under unstable conditions. We found that dimensionless statistics related to the rate of dissipation of turbulence kinetic energy (TKE) and the scalar variance display significant departures from MOST as expected, whereas the vertical velocity variance follows MOST much more closely. Much better agreement between the dimensionless statistics with the Obukhov similarity variable, however, was found for the subset of measurements made at a low zenith angle Z, in the range 0°&#8239;&#8201;<&#8201;&#8239;|Z|&#8239;&#8201;<&#8201;&#8239;20°. We conjecture that this improvement is due to the relationship between sunlight incidence and the ?activation?deactivation? of scalar sinks and sources vertically distributed in the forest. Finally, we evaluated the relaxation coefficient of relaxed eddy accumulation: it is also affected by zenith angle, with considerable improvement in the range 0°&#8239;&#8201;<&#8201;&#8239;|Z|&#8239;&#8201;<&#8201;&#8239;20°, and its values fall within the range reported in the literature for the unstable surface layer. In general, our results indicate the possibility of better stability-derived flux estimates for low zenith angle ranges

VEHICLE OCCLUSION REMOVAL FROM SINGLE AERIAL IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

Removing occluding objects such as vehicles from drivable areas allows precise extraction of road boundaries and related semantic objects such as lane-markings, which is crucial for several applications such as generating high-definition maps for autonomous driving. Conventionally, multiple images of the same area taken at different times or from various perspectives are used to remove occlusions and to reconstruct the occluded areas. Nevertheless, these approaches require large amounts of data, which are not always available. Furthermore, they do not work for static occlusions caused by, among others, parked vehicles. In this paper, we address occlusion removal based on single aerial images using generative adversarial networks (GANs), which are able to deal with the mentioned challenges. To this end, we adapt several state-of-the-art GAN-based image inpainting algorithms to reconstruct the missing information. Results indicate that the StructureFlow algorithm outperforms the competitors and the restorations obtained are robust, with high visual fidelity in real-world applications. Furthermore, due to the lack of annotated aerial vehicle removal datasets, we generate a new dataset for training and validating the algorithms, the Aerial Vehicle Occlusion Removal (AVOR) dataset. To the best of our knowledge, our work is the first to address vehicle removal using deep learning algorithms to enhance maps

Natural formation of chloro- and bromoacetone in salt lakes of Western Australia

Western Australia is a semi-/arid region known for saline lakes with a wide range of geochemical parameters (pH 2.5-7.1, Cl- 10-200 g L-1. This study reports on the haloacetones chloro- and bromoacetone in air over 6 salt lake shorelines. Significant emissions of chloroacetone (up to 0.2 µmol m-2 h-1) and bromoacetone (up to 1. 5 µmol m-2 h-1) were detected, and a photochemical box model was employed to evaluate the contribution of their atmospheric formation from the olefinic hydrocarbons propene and methacrolein in the gas phase. The measured concentrations could not explain the photochemical halogenation reaction, indicating a strong hitherto unknown source of haloacetones. Aqueous-phase reactions of haloacetones, investigated in the laboratory using humic acid in concentrated salt solutions, were identified as alternative formation pathway by liquid-phase reactions, acid catalyzed enolization of ketones, and subsequent halogenation. In order to verify this mechanism, we made measurements of the Henry's law constants, rate constants for hydrolysis and nucleophilic exchange with chloride, UV-spectra and quantum yields for the photolysis of bromoacetone and 1,1-dibromoacetone in the aqueous phase. We suggest that heterogeneous processes induced by humic substances in the quasi-liquid layer of the salt crust, particle surfaces and the lake water are the predominating pathways for the formation of the observed haloacetones

Surface-atmosphere exchange of inorganic water-soluble gases and associated ions in bulk aerosol above agricultural grassland pre- and post- fertilisation

The increasing use of intensive agricultural practices can lead to damaging consequences for the atmosphere through enhanced emissions of air pollutants. However, there are few direct measurements of the surface–atmosphere exchange of trace gases and water-soluble aerosols over agricultural grassland, particularly of reactive nitrogen compounds. In this study, we present measurements of the concentrations, fluxes and deposition velocities of the trace gases HCl, HONO, HNO3, SO2 and NH3 as well as their associated water-soluble aerosol counterparts Cl−, NO2−, NO3−, SO42− and NH4+ as determined hourly for 1 month in May–June 2016 over agricultural grassland near Edinburgh, UK, pre- and postfertilisation. Measurements were made using the Gradient of Aerosols and Gases Online Registrator (GRAEGOR) wet-chemistry two-point gradient instrument. Emissions of NH3 peaked at 1460ng m−2 s−1 3h after fertilisation, with an emission of HONO peaking at 4.92ng m−2 s−1 occurring 5h after fertilisation. Apparent emissions of NO3− aerosol were observed after fertilisation which, coupled with a divergence of HNO3 deposition velocity (Vd) from its theoretical maximum value, suggested the reaction of emitted NH3 with atmospheric HNO3 to form ammonium nitrate aerosol. The use of the conservative exchange fluxes of tot-NH4+ and tot-NO3− indicated net emission of tot-NO3−, implying a ground source of HNO3 after fertilisation. Daytime concentrations of HONO remained above the detection limit (30ng m−3) throughout the campaign, suggesting a daytime source for HONO at the site. Whilst the mean Vd of NH4+ was 0.93mm s−1 in the range expected for the accumulation mode, the larger average Vd for Cl− (3.65mm s−1), NO3− (1.97mm s−1) and SO42− (1.89mm s−1) reflected the contribution of a super-micron fraction and decreased with increasing PM2.5∕PM10 ratio (a proxy measurement for aerosol size), providing evidence – although limited by the use of a proxy for aerosol size – of a size dependence of aerosol deposition velocity for aerosol chemical compounds, which has been suggested from process-orientated models of aerosol deposition