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    SVM classification of multispectral data

    AdamL
    By AdamL,
    Hello everyone,   I am using Support Vector Machine (SVM) classifier on ASTER and Sentinel-2 datasets to map geological units. I am familiar with the basic statistical concept of SVM classifier, but in more practical terms, how does it use the spectral data from the satellite imagery to classify different classes? How does it determine the most accurate fit from the image-based training dataset?  Thank you to all of you, Cheers!

    Hands-on Workshop on WebGIS and Mapping

    Profound
    By Profound,
    Hands-on Workshop on WebGIS and Mapping 11th May 2020 to 22nd May 2020. Web GIS is a new pattern for delivering GIS capabilities on the web. Maps on the web provide a new paradigm for how people everywhere access and use geographic information. They use GIS maps on their desktops, the web, tablets, and smartphones for a sophisticated range of activities to apply advanced geographic information. Web GIS is a transformation of GIS that brings analytics to spatial data in a way that wasn’

    Covid-19

    intertronic
    By intertronic,
    for anyone who works with COVID-19, you can get a 6-month free ArcGIS licence https://www.esri.com/en-us/disaster-response/request-assistance Info  To help public health agencies and other organizations jump-start their response to coronavirus disease 2019 (COVID-19), Esri is providing the ArcGIS Hub Coronavirus Response template at no cost along with a complimentary six-month ArcGIS Online subscription with ArcGIS Hub Basic and ArcGIS Insights. The ArcGIS Hub Coronavirus Res

    Mapzen open-source mapping project revived under the Urban Computing Foundation

    Lurker
    By Lurker,
    The Mapzen open-source mapping platform has a hard history. On the one hand, Mapzen, which is based on OpenStreetMap, is used by over 70,000 developers and it's the backbone of such mapping services as , Remix and Carto. But, as a business, Mapzen failed in 2018. Mapzen's code and service lived on as a Linux Foundation Project.  Now, it's moved on to the Urban Computing Foundation (UCF), another Linux Foundation group with more resources. UCF is devoted to helping create smarter cities, mul

    arcgis pro flood simutation

    jusk2000
    By jusk2000,
    Hi guys, I need to create a flood simulation on arcgis pro,  does anyone know how to do that  or could point me to some tutorial ?    There are many tutorials on youtube using old versions of arcgis ( before "pro" ),   I could not find anything for arcgis pro.  I am using arcgis pro 2.2.3 Thanks

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    • Hi Anyone has Sarscape V5.6 installer Thanks
    • which one that works? i have valid licence for single basic but it only support 10.1-10.8 not 10.8.1
    • From space, large decks of closely spaced stratocumulus clouds appear like bright cotton balls hovering over the ocean. They cover vast areas—literally thousands of miles of the subtropical oceans—and linger for weeks to months. Because these marine clouds reflect more solar radiation than the surface of the ocean, cooling the Earth's surface, the lifetime of stratocumulus clouds is an important component of the Earth's radiation balance. It is necessary, then, to accurately represent cloud lifetimes in the earth system models (ESM) used to predict future climate conditions. Turbulence—air motions occurring at small scales—is primarily responsible for the longevity of marine stratocumulus clouds. Drizzle—precipitation comprising water droplets smaller than half a millimeter in diameter—is constantly present within and below these marine cloud systems. Because these tiny drops affect and are affected by turbulence below marine clouds, scientists need to know more about how drizzle affects turbulence in these clouds to enable more accurate climate forecasts. A team led by Virendra Ghate, an atmospheric scientist, and Maria Cadeddu, a principal atmospheric research engineer in the Environmental Science division at the U.S. Department of Energy's (DOE) Argonne National Laboratory, has been studying the impact of drizzle inside marine clouds since 2017. Their unique data set caught the attention of researchers at DOE's Lawrence Livermore National Laboratory. About three years ago, a collaborator from Livermore, which led national efforts to improve cloud representation in climate models, called for observational studies focusing on drizzle-turbulence interactions. Such studies did not exist at that time because of the limited set of observations and lack of techniques to derive all the geophysical properties of concern. "The analysis of the developed dataset allowed us to show that drizzle decreases turbulence below stratocumulus clouds—something that was only shown by model simulations in the past," said Ghate. "The richness of the developed data will allow us to address several fundamental questions regarding drizzle-turbulence interactions in the future."   The Argonne team set out to characterize the clouds' properties using observations at the Atmospheric Radiation Measurement (ARM)'s Eastern North Atlantic site, a DOE Office of Science User Facility, and data from instruments on board geostationary and polar‐orbiting satellites. The instruments collect engineering variables, such as voltages and temperatures. The team combined measurements from different instruments to derive properties of the water vapor and drizzle in and below the clouds.   Ghate and Cadeddu were interested in geophysical variables, such as cloud water content, drizzle particle size and others. So they developed a novel algorithm that synergistically retrieved all the necessary parameters involved in drizzle-turbulence interactions. The algorithm uses data from several ARM instruments—including radar, lidar and radiometer—to derive the geophysical variables of interest: size (or diameter) of precipitation drops, amount of liquid water corresponding to cloud drops, and precipitation drops. Using the data from ARM, Ghate and Cadeddu derived these parameters, subsequently publishing three observational studies that focused on two different spatial organizations of stratocumulus clouds to characterize the drizzle-turbulence interactions in these cloud systems. Their results led to a collaborative effort with modelers from Livermore. In that effort, the team used observations to improve the representation of drizzle-turbulence interactions in DOE's Energy Exascale Earth System Model (E3SM). "The observational references from Ghate and Cadeddu's retrieval technique helped us determine that version 1 of E3SM produces unrealistic drizzle processes. Our collaborative study implies that comprehensive examinations of the modeled cloud and drizzle processes with observational references are needed for current climate models," said Xue Zheng, a staff scientist in the Atmospheric, Earth, and Energy division at Livermore. Said Cadeddu: "Generally, the unique expertise here at the lab is attributable to our ability to go from the raw data to the physical parameters and from there to the physical processes in the clouds. The data and the instruments themselves are very difficult to use because they are mostly remote sensors that don't directly measure what we need (e.g., rain rate or liquid water path); instead, they measure electromagnetic properties such as backscatter, Doppler spectra and radiance. In addition, the raw signal is often affected by artifacts, noise, aerosols and precipitation. The raw data are either directly related to the physical quantities we want to measure through well-defined sets of equations, or they are indirectly related. In the latter case, deriving the physical quantities means solving mathematical equations called 'inverse problems' which, by themselves, are complicated. The fact that we have been able to develop new ways to quantify the physical properties of the clouds and extract reliable information about them is a major achievement. And it has put us at the forefront of research on these types of clouds." Because they have focused only on the few aspects of the complex drizzle-turbulence interactions, Ghate and Cadeddu plan to continue their research. They also intend to focus on other regions such as the North Pacific and South Atlantic oceans, where the cloud, drizzle and turbulence properties differ vastly from those in the North Atlantic. source: https://phys.org/news/2021-03-algorithm-capture-drizzle-turbulence-interactions-future.html
    • amazing share dude!! thank 
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