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Python module for geostatistical modeling, designed for mineral resource estimation Opengeostat Consulting MIT/GPL Windows, Linux and OSX Fortran 95, Cython and Python It has functions for drillhole calculations, block modeling, wireframing and geostatistics with modified gslib code linked into python gstlearn [6]
MonetDB is an open-source column-oriented relational database management system (RDBMS) originally developed at the Centrum Wiskunde & Informatica (CWI) in the Netherlands.It is designed to provide high performance on complex queries against large databases, such as combining tables with hundreds of columns and millions of rows.
FeatureOfInterest: An Observation results in a value being assigned to a phenomenon.The phenomenon is a property of a feature, the latter being the FeatureOfInterest of the Observation. [ 9 ] In addition to the above sensing resources, SensorThings API Part II - Tasking Core defines the following resources: [ 10 ]
The components of the model use the Earth System Modeling Framework (ESMF), enabling them to be connected in a flexible manner and supporting the investigation of many different aspects of Earth science, in particular questions related to coupled processes involving the atmosphere, ocean, and/or land. Uses of GEOS span a range of spatiotemporal ...
The Earth Observing System Data and Information System (EOSDIS) is a key core capability in NASA's Earth Science Data Systems Program. Designed and maintained by Raytheon Intelligence & Space, it is a comprehensive data and information system designed to perform a wide variety of functions in support of a heterogeneous national and international user community.
Earth observation (EO) is the gathering of information about the physical, chemical, and biological systems of the planet Earth. [1] It can be performed via remote-sensing technologies (Earth observation satellites) or through direct-contact sensors in ground-based or airborne platforms (such as weather stations and weather balloons, for example).
[1] [2] Its goal is to apply machine learning for Earth observation [3] to meet the Sustainable Development Goals. [4] The foundation works on developing openly licensed Earth observation machine learning libraries, training data sets [ 5 ] and models through an open source hub [ 6 ] that support missions worldwide [ 7 ] like agriculture, [ 8 ...
Earth observations are vital for policymaking and assessment in many fields. GEO focuses on facilitating access to Earth observation data for nine priority areas: natural and human-induced disasters, environmental sources of health hazards, energy management, climate change and its impacts, freshwater resources, weather forecasting, ecosystem ...