INFACT technology watch report

This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement nº 776487. Furthermore, some of the authors (B.J. and V.H.-S.) were supported by the Spanish Ministry of Science Innovation and Universities under the framework of the R&D project RTI2018-098966-B-I00.Summary: This report presents a bibliometric study on patents and scientific publications related to the following technologies involved in INFACT: airborne electromagnetic methods, airborne gravity gradiometry, airborne magnetometry and drone-borne hyperspectral imaging. A statistical analysis of the documents reveals the main players, technology trends and collaboration patterns via bibliometric techniques

Social acceptance of green hydrogen in Germany: building trust through responsible innovation

Background Social acceptance presents a major challenge for Germany’s transition to green energy. As a power-to-x technology, green hydrogen is set to become a key component of a future sustainable energy system. With a view to averting conflicts like those surrounding wind energy, we have investigated social acceptance of green hydrogen at an early stage in its implementation, before wider rollout. Our study uses a mixed-method approach, wherein semi-structured interviews (n = 24) and two participatory workshops (n = 51) in a selected region in central Germany serve alongside a representative survey (n = 2054) as the basis for both understanding social attitudes and reaching generalisable conclusions. Results Overall, it is possible to observe both a marked lack of knowledge and a large degree of openness towards green hydrogen and its local use, along with high expectations regarding environmental and climate protection. We reach three key conclusions. First, acceptance of green hydrogen relies on trust in science, government, the media, and institutions that uphold distributive justice, with consideration for regional values playing a vital role in establishing said trust. Second, methodologically sound participatory processes can promote acceptance, and active support in particular. Third, recurrent positive participatory experiences can effectively foster trust. Conclusions Accordingly, we argue that trust should be strengthened on a structural level, and that green hydrogen acceptance should be understood as a matter of responsible innovation. As the first empirical investigation into social acceptance of green hydrogen, and by conceptually interlinking acceptance research and responsible innovation, this study constitutes an important contribution to existing research

Radiometric Correction and 3D Integration of Long-Range Ground-Based Hyperspectral Imagery for Mineral Exploration of Vertical Outcrops

Recently, ground-based hyperspectral imaging has come to the fore, supporting the arduous task of mapping near-vertical, difficult-to-access geological outcrops. The application of outcrop sensing within a range of one to several hundred metres, including geometric corrections and integration with accurate terrestrial laser scanning models, is already developing rapidly. However, there are few studies dealing with ground-based imaging of distant targets (i.e., in the range of several kilometres) such as mountain ridges, cliffs, and pit walls. In particular, the extreme influence of atmospheric effects and topography-induced illumination differences have remained an unmet challenge on the spectral data. These effects cannot be corrected by means of common correction tools for nadir satellite or airborne data. Thus, this article presents an adapted workflow to overcome the challenges of long-range outcrop sensing, including straightforward atmospheric and topographic corrections. Using two datasets with different characteristics, we demonstrate the application of the workflow and highlight the importance of the presented corrections for a reliable geological interpretation. The achieved spectral mapping products are integrated with 3D photogrammetric data to create large-scale now-called “hyperclouds”, i.e., geometrically correct representations of the hyperspectral datacube. The presented workflow opens up a new range of application possibilities of hyperspectral imagery by significantly enlarging the scale of ground-based measurements

Integration of Vessel-Based Hyperspectral Scanning and 3D-Photogrammetry for Mobile Mapping of Steep Coastal Cliffs in the Arctic

Remote and extreme regions such as in the Arctic remain a challenging ground for geological mapping and mineral exploration. Coastal cliffs are often the only major well-exposed outcrops, but are mostly not observable by air/spaceborne nadir remote sensing sensors. Current outcrop mapping efforts rely on the interpretation of Terrestrial Laser Scanning and oblique photogrammetry, which have inadequate spectral resolution to allow for detection of subtle lithological differences. This study aims to integrate 3D-photogrammetry with vessel-based hyperspectral imaging to complement geological outcrop models with quantitative information regarding mineral variations and thus enables the differentiation of barren rocks from potential economic ore deposits. We propose an innovative workflow based on: (1) the correction of hyperspectral images by eliminating the distortion effects originating from the periodic movements of the vessel; (2) lithological mapping based on spectral information; and (3) accurate 3D integration of spectral products with photogrammetric terrain data. The method is tested using experimental data acquired from near-vertical cliff sections in two parts of Greenland, in Karrat (Central West) and Søndre Strømfjord (South West). Root-Mean-Square Error of (6.7, 8.4) pixels for Karrat and (3.9, 4.5) pixels for Søndre Strømfjord in X and Y directions demonstrate the geometric accuracy of final 3D products and allow a precise mapping of the targets identified using the hyperspectral data contents. This study highlights the potential of using other operational mobile platforms (e.g., unmanned systems) for regional mineral mapping based on horizontal viewing geometry and multi-source and multi-scale data fusion approaches

Tinto: Multisensor Benchmark for 3-D Hyperspectral Point Cloud Segmentation in the Geosciences

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2-D image data, which is insufficient for 3-D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multisensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for nonstructured 3-D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground truth. The point cloud is dense and contains 3242964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3-D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256

Evolutionary significance of inversions in legume chloroplast DNAs

Cloned genes from tobacco, spinach, and pea were used as hybridization probes to localize 36 protein genes on the chloroplast chromosomes of four legumes — mung bean, common bean, soybean, and pea. The first three chloroplast DNAs (cpDNAs), all of which retain a large inverted repeat, have an identical gene order with but one exception. A 78 kb segment encompassing nearly the entire large single copy region is inverted in mung bean and common bean relative to soybean and non-legumes. The simplest evolutionary explanation for this difference is a 78 kb inversion, with one endpoint between rps8 and inf A and the second between psb A and rpl2 . However, we can not rule out a two-step re-arrangement (consisting of successive expansion and contraction of the inverted repeat) leading to the relocation of a block of six ribosomal protein genes ( rps 19- rps 8) from one end of the large single copy region to the other. Analysis of gene locations in pea cpDNA, which lacks the large inverted repeat, combined with cross-hybridization studies using 59 clones covering the mung bean genome, leads to a refined picture of the position and nature of the numerous rearrangements previously described in the pea genome. A minimum of eight large inversions are postulated to account for these rearrangements. None of these inversions disrupt groups of genes that are transcriptionally linked in angiosperm cpDNA. Rather, the end-points of inversions are associated with relatively spacer-rich segments of the genome, many of which contain tRNA genes. All of the pea-specific inversions are shown to be positionally distinct from those recently described in a closely related legume, broad bean.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46965/1/294_2004_Article_BF00405856.pd