5,385 research outputs found

    Application of ERTS-1 data to integrated state planning in the state of Maryland

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    There are no author-identified significant results in this report

    The comparative evaluation of ERTS-1 imagery for resource inventory in land use planning

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    The author has identified the following significant results. Multidiscipline team interpretation and mapping of resources for Crook County is complete on 1:250,000 scale enlargements of ERTS imagery and 1:120,000 hi-flight photography. Maps of geology, soils, vegetation-land use and land resources units were interpreted to show limitations, suitabilities, and geologic hazards for land use planning. Mapping of lineaments and structures from ERTS imagery has shown a number of features not previously mapped in Oregon. A multistage timber inventory of Ochoco National Forest was made, using ERTS images as the first stage. Inventory of forest clear-cutting practices was successfully demonstrated with color composites. Soil tonal differences in fallow fields correspond with major soil boundaries in loess-mantled terrain. A digital classification system used for discriminating natural vegetation and geologic material classes was successful in separating most major classes around Newberry Caldera, Mt. Washington, and Big Summit Prairie

    VGC 2023 - Unveiling the dynamic Earth with digital methods: 5th Virtual Geoscience Conference: Book of Abstracts

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    Conference proceedings of the 5th Virtual Geoscience Conference, 21-22 September 2023, held in Dresden. The VGC is a multidisciplinary forum for researchers in geoscience, geomatics and related disciplines to share their latest developments and applications.:Short Courses 9 Workshops Stream 1 10 Workshop Stream 2 11 Workshop Stream 3 12 Session 1 – Point Cloud Processing: Workflows, Geometry & Semantics 14 Session 2 – Visualisation, communication & Teaching 27 Session 3 – Applying Machine Learning in Geosciences 36 Session 4 – Digital Outcrop Characterisation & Analysis 49 Session 5 – Airborne & Remote Mapping 58 Session 6 – Recent Developments in Geomorphic Process and Hazard Monitoring 69 Session 7 – Applications in Hydrology & Ecology 82 Poster Contributions 9

    Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

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    This Special Issue, focusing on the value of mineralogical monitoring for the mining and minerals industry, should include detailed investigations and characterizations of minerals and ores of the following fields for ore and process control: Lithium ores—determination of lithium contents by XRD methods; Copper ores and their different mineralogy; Nickel lateritic ores; Iron ores and sinter; Bauxite and bauxite overburden; Heavy mineral sands. The value of quantitative mineralogical analysis, mainly by XRD methods, combined with other techniques for the evaluation of typical metal ores and other important minerals, will be shown and demonstrated for different minerals. The different steps of mineral processing and metal contents bound to different minerals will be included. Additionally, some processing steps, mineral enrichments, and optimization of mineral determinations using XRD will be demonstrated. Statistical methods for the treatment of a large set of XRD patterns of ores and mineral concentrates, as well as their value for the characterization of mineral concentrates and ores, will be demonstrated. Determinations of metal concentrations in minerals by different methods will be included, as well as the direct prediction of process parameters from raw XRD data

    A review of artificial intelligence technologies in mineral identification : classification and visualization

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    Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.The National Natural Science Foundation of China.https://www.mdpi.com/journal/jsanElectrical, Electronic and Computer Engineerin

    An approach based on Landsat images for shoreline monitoring to support integrated coastal management - a case study, Ezbet Elborg, Nile Delta, Egypt

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    Monitoring the dynamic behavior of shorelines is an essential factor for integrated coastal management (ICM). In this study, satellite-derived shorelines and corresponding eroded and accreted areas of coastal zones have been calculated and assessed for 15 km along the coasts of Ezbet Elborg, Nile Delta, Egypt. A developed approach is designed based on Landsat satellite images combined with GIS to estimate an accurate shoreline changes and study the effect of seawalls on it. Landsat images for the period from 1985 to 2018 are rectified and classified using Supported Vector Machines (SVMs) and then processed using ArcGIS to estimate the effectiveness of the seawall that was constructed in year 2000. Accuracy assessment results show that the SVMs improve images accuracy up to 92.62% and the detected shoreline by the proposed method is highly correlated (0.87) with RTK-GPS measurements. In addition, the shoreline change analysis presents that a dramatic erosion of 2.1 km2 east of Ezbet Elborg seawall has occurred. Also, the total accretion areas are equal to 4.40 km2 and 10.50 km2 in between 1985-and-2000 and 2000-and-2018, respectively, along the southeast side of the study area

    Advances in Computational Intelligence Applications in the Mining Industry

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    This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

    Quarterly literature review of the remote sensing of natural resources

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    The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

    Integrated Applications of Geo-Information in Environmental Monitoring

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    This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

    The Need for Accurate Pre-processing and Data Integration for the Application of Hyperspectral Imaging in Mineral Exploration

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    Die hyperspektrale Bildgebung stellt eine Schlüsseltechnologie in der nicht-invasiven Mineralanalyse dar, sei es im Labormaßstab oder als fernerkundliche Methode. Rasante Entwicklungen im Sensordesign und in der Computertechnik hinsichtlich Miniaturisierung, Bildauflösung und Datenqualität ermöglichen neue Einsatzgebiete in der Erkundung mineralischer Rohstoffe, wie die drohnen-gestützte Datenaufnahme oder digitale Aufschluss- und Bohrkernkartierung. Allgemeingültige Datenverarbeitungsroutinen fehlen jedoch meist und erschweren die Etablierung dieser vielversprechenden Ansätze. Besondere Herausforderungen bestehen hinsichtlich notwendiger radiometrischer und geometrischer Datenkorrekturen, der räumlichen Georeferenzierung sowie der Integration mit anderen Datenquellen. Die vorliegende Arbeit beschreibt innovative Arbeitsabläufe zur Lösung dieser Problemstellungen und demonstriert die Wichtigkeit der einzelnen Schritte. Sie zeigt das Potenzial entsprechend prozessierter spektraler Bilddaten für komplexe Aufgaben in Mineralexploration und Geowissenschaften.Hyperspectral imaging (HSI) is one of the key technologies in current non-invasive material analysis. Recent developments in sensor design and computer technology allow the acquisition and processing of high spectral and spatial resolution datasets. In contrast to active spectroscopic approaches such as X-ray fluorescence or laser-induced breakdown spectroscopy, passive hyperspectral reflectance measurements in the visible and infrared parts of the electromagnetic spectrum are considered rapid, non-destructive, and safe. Compared to true color or multi-spectral imagery, a much larger range and even small compositional changes of substances can be differentiated and analyzed. Applications of hyperspectral reflectance imaging can be found in a wide range of scientific and industrial fields, especially when physically inaccessible or sensitive samples and processes need to be analyzed. In geosciences, this method offers a possibility to obtain spatially continuous compositional information of samples, outcrops, or regions that might be otherwise inaccessible or too large, dangerous, or environmentally valuable for a traditional exploration at reasonable expenditure. Depending on the spectral range and resolution of the deployed sensor, HSI can provide information about the distribution of rock-forming and alteration minerals, specific chemical compounds and ions. Traditional operational applications comprise space-, airborne, and lab-scale measurements with a usually (near-)nadir viewing angle. The diversity of available sensors, in particular the ongoing miniaturization, enables their usage from a wide range of distances and viewing angles on a large variety of platforms. Many recent approaches focus on the application of hyperspectral sensors in an intermediate to close sensor-target distance (one to several hundred meters) between airborne and lab-scale, usually implying exceptional acquisition parameters. These comprise unusual viewing angles as for the imaging of vertical targets, specific geometric and radiometric distortions associated with the deployment of small moving platforms such as unmanned aerial systems (UAS), or extreme size and complexity of data created by large imaging campaigns. Accurate geometric and radiometric data corrections using established methods is often not possible. Another important challenge results from the overall variety of spatial scales, sensors, and viewing angles, which often impedes a combined interpretation of datasets, such as in a 2D geographic information system (GIS). Recent studies mostly referred to work with at least partly uncorrected data that is not able to set the results in a meaningful spatial context. These major unsolved challenges of hyperspectral imaging in mineral exploration initiated the motivation for this work. The core aim is the development of tools that bridge data acquisition and interpretation, by providing full image processing workflows from the acquisition of raw data in the field or lab, to fully corrected, validated and spatially registered at-target reflectance datasets, which are valuable for subsequent spectral analysis, image classification, or fusion in different operational environments at multiple scales. I focus on promising emerging HSI approaches, i.e.: (1) the use of lightweight UAS platforms, (2) mapping of inaccessible vertical outcrops, sometimes at up to several kilometers distance, (3) multi-sensor integration for versatile sample analysis in the near-field or lab-scale, and (4) the combination of reflectance HSI with other spectroscopic methods such as photoluminescence (PL) spectroscopy for the characterization of valuable elements in low-grade ores. In each topic, the state of the art is analyzed, tailored workflows are developed to meet key challenges and the potential of the resulting dataset is showcased on prominent mineral exploration related examples. Combined in a Python toolbox, the developed workflows aim to be versatile in regard to utilized sensors and desired applications
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