Mapping with Skysat Images

The very high-resolution space imagery now competes with some functions that were previously solved with aerial images. Several very high-resolution optical satellites with a ground sampling distance (GSD) of 1 m and smaller are currently active. Not all of these satellites take images worldwide. Nevertheless, it is not a problem to obtain up-to-date satellite images with a very high resolution. Mapping projects not only need to consider access and quality, but also cost-effectiveness. Of course, the economic framework conditions are decisive for the decision as to whether space images or very high-resolution satellite images should be used. With a total 21 SkySat satellites, low-cost satellites with very high resolution have changed the economic conditions. To keep costs and weights down, the Skysat satellites were not designed to offer the best direct geo-referencing performance, but this problem can be solved by automatic orientation in relation to existing orthoimages.In North Rhine-Westphalia, the cadastral maps must be checked at regular intervals to ensure that the buildings are complete. A test project examined whether this is possible with SkySat images. The geometric conditions and the image quality with the effective ground resolutions are investigated. Experiences from earlier publications could not be used. First the specific problem had to be solved, the resolution of the SkySat images was improved by lowering the satellite orbit altitude from 500 km to 450 km and by a better super resolution with 0.50 m ground sampling distance for the SkySat Collect orthoimages and in addition Planet improved their generation of Collect images. The required standard deviation of the object details of 4 m was achieved clearly as the effective ground resolution of 0.5 m if the angle of incidence is below 20°

Residual Shuffling Convolutional Neural Networks for Deep Semantic Image Segmentation Using Multi-Modal Data

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation

Analysis and bias improvement of height models based on satellite images

Height models are a fundamental part of the geo-information required for various applications. The determination of height models by aerial photogrammetry, LiDAR or space images is time-consuming and expensive. For height models with large area coverage, UAVs are not economic. The freely available height models ASTER GDEM-3, SRTM, AW3D30 and TDM90 can meet various requirements. With the exception of ASTER-GDEM-3, which cannot compete with the other, the digital surface models SRTM, AW3D30 and TDM90 are analyzed in detail for accuracy and morphology in 4 test sites using LiDAR reference DTMs. The accuracy figures root mean square error, standard deviation, NMAD and LE90 are compared as well as the accuracy dependence on the terrain inclination. The analysis uses a layer for the open areas, excluding forest and settlement areas. Remaining elements that do not belong to a DTM are filtered. Particular attention is paid to systematic errors. The InSAR height models SRTM and TDM90 have some accuracy and morphological restrictions in mountain and settlement areas. Even so, the direct sensor orientation of TDM90 is better than for the other. Optimal results in terms of accuracy and morphology were achieved with AW3D30 corrected by TDM90 for the local absolute height level. This correction reduces the bias and also the tilt of the height models compared to the reference LiDAR DTM

SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION

In this paper, we address the semantic segmentation of aerial imagery based on the use of multi-modal data given in the form of true orthophotos and the corresponding Digital Surface Models (DSMs). We present the Deeply-supervised Shuffling Convolutional Neural Network (DSCNN) representing a multi-scale extension of the Shuffling Convolutional Neural Network (SCNN) with deep supervision. Thereby, we take the advantage of the SCNN involving the shuffling operator to effectively upsample feature maps and then fuse multiscale features derived from the intermediate layers of the SCNN, which results in the Multi-scale Shuffling Convolutional Neural Network (MSCNN). Based on the MSCNN, we derive the DSCNN by introducing additional losses into the intermediate layers of the MSCNN. In addition, we investigate the impact of using different sets of hand-crafted radiometric and geometric features derived from the true orthophotos and the DSMs on the semantic segmentation task. For performance evaluation, we use a commonly used benchmark dataset. The achieved results reveal that both multi-scale fusion and deep supervision contribute to an improvement in performance. Furthermore, the use of a diversity of hand-crafted radiometric and geometric features as input for the DSCNN does not provide the best numerical results, but smoother and improved detections for several objects

Vanadium (β-(Dimethylamino)ethyl)cyclopentadienyl Complexes with Diphenylacetylene Ligands

Reduction of the V(III) (β-(dimethylamino)ethyl)cyclopentadienyl dichloride complex [η5:η1-C5H4(CH2)2NMe2]VCl2(PMe3) with 1 equiv of Na/Hg yielded the V(II) dimer {[η5:η1-C5H4(CH2)2NMe2]V(µ-Cl)}2 (2). This compound reacted with diphenylacetylene in THF to give the V(II) alkyne adduct [η5:η1-C5H4(CH2)2NMe2]VCl(η2-PhC≡CPh). Further reduction of 2 with Mg in the presence of diphenylacetylene resulted in oxidative coupling of two diphenylacetylene groups to yield the diamagnetic, formally V(V), bent metallacyclopentatriene complex [η5:η1-C5H4(CH2)2NMe2]V(C4Ph4).

An artificial impact on the asteroid (162173) Ryugu formed a crater in the gravity-dominated regime

The Hayabusa2 spacecraft investigated the small asteroid Ryugu, which has a rubble-pile structure. We describe an impact experiment on Ryugu using Hayabusa2’s Small Carry-on Impactor. The impact produced an artificial crater with a diameter >10 meters, which has a semicircular shape, an elevated rim, and a central pit. Images of the impact and resulting ejecta were recorded by the Deployable CAMera 3 for >8 minutes, showing the growth of an ejecta curtain (the outer edge of the ejecta) and deposition of ejecta onto the surface. The ejecta curtain was asymmetric and heterogeneous and it never fully detached from the surface. The crater formed in the gravity-dominated regime; in other words, crater growth was limited by gravity not surface strength. We discuss implications for Ryugu’s surface age

Effects of Impact and Target Parameters on the Results of a Kinetic Impactor: Predictions for the Double Asteroid Redirection Test (DART) Mission

The Double Asteroid Redirection Test (DART) spacecraft will impact into the asteroid Dimorphos on 2022 September 26 as a test of the kinetic impactor technique for planetary defense. The efficiency of the deflection following a kinetic impactor can be represented using the momentum enhancement factor, β, which is dependent on factors such as impact geometry and the specific target material properties. Currently, very little is known about Dimorphos and its material properties, which introduces uncertainty in the results of the deflection efficiency observables, including crater formation, ejecta distribution, and β. The DART Impact Modeling Working Group (IWG) is responsible for using impact simulations to better understand the results of the DART impact. Pre-impact simulation studies also provide considerable insight into how different properties and impact scenarios affect momentum enhancement following a kinetic impact. This insight provides a basis for predicting the effects of the DART impact and the first understanding of how to interpret results following the encounter. Following the DART impact, the knowledge gained from these studies will inform the initial simulations that will recreate the impact conditions, including providing estimates for potential material properties of Dimorphos and β resulting from DART’s impact. This paper summarizes, at a high level, what has been learned from the IWG simulations and experiments in preparation for the DART impact. While unknown, estimates for reasonable potential material properties of Dimorphos provide predictions for β of 1–5, depending on end-member cases in the strength regime

After DART: Using the First Full-scale Test of a Kinetic Impactor to Inform a Future Planetary Defense Mission

NASA’s Double Asteroid Redirection Test (DART) is the first full-scale test of an asteroid deflection technology. Results from the hypervelocity kinetic impact and Earth-based observations, coupled with LICIACube and the later Hera mission, will result in measurement of the momentum transfer efficiency accurate to ∼10% and characterization of the Didymos binary system. But DART is a single experiment; how could these results be used in a future planetary defense necessity involving a different asteroid? We examine what aspects of Dimorphos’s response to kinetic impact will be constrained by DART results; how these constraints will help refine knowledge of the physical properties of asteroidal materials and predictive power of impact simulations; what information about a potential Earth impactor could be acquired before a deflection effort; and how design of a deflection mission should be informed by this understanding. We generalize the momentum enhancement factor β, showing that a particular direction-specific β will be directly determined by the DART results, and that a related direction-specific β is a figure of merit for a kinetic impact mission. The DART β determination constrains the ejecta momentum vector, which, with hydrodynamic simulations, constrains the physical properties of Dimorphos’s near-surface. In a hypothetical planetary defense exigency, extrapolating these constraints to a newly discovered asteroid will require Earth-based observations and benefit from in situ reconnaissance. We show representative predictions for momentum transfer based on different levels of reconnaissance and discuss strategic targeting to optimize the deflection and reduce the risk of a counterproductive deflection in the wrong direction