My EOS Vision
Introduction to EOS Vision
EOS Vision – is a powerful cloud-based geodata analysis tool to help analysts gain insights from location data and deliver business outcomes. To visualize data sets it creates data-driven maps with SQL-based interface and state-of-the-art cartographic tools. The instrument is integrated into EOS Platform and is able to run a full-circle of geodata processing workflow in one suit.
The leading features of the instrument are:
- Spatial data processing – display, edit, create
- SQL interface with PostGIS functions support
- Data stylization settings
- A full range of the most widely-used geodata formats support
- Data publishing option
- Cloud-based data management
The USP of the instrument is based on:
- Advanced visualization techniques – a combination of classic and state-of-the-art data visualization instruments
- Ability to work with geodata – it uses Amazon servers, bringing faster results
- Multifunctionality – a full-fledged tool for working with spatial data.
Integration with EOS Platform
Being a part of an ecosystem of four mutually integrated EOS products, namely
- LandViewer allows even non-expert users to search, view, instantly process and download satellite multichannel images.
- EOS Processing delivers remote sensing analytics that provides a unique suite of algorithms that extract meaningful information, using preset chains and neural networks.
- EOS Storage is speciallly developed to store, share, distribute and get the instant access to thousands of Terabytes of Earth Observation and GIS related data.
the instruments provides you with a one-stop solution for geodata ananlysis according to the follolwing procedure:
Search, process on-the-fly and pick an appropriate image from the largest satellite imagery catalogue in LandViewer, save it to your personal (256Gb) cloud and get the instant access, share and distribute huge GIS related data in EOS Storage, extract meaningful information from the image with Net-based neural algorithms in EOS Processing and perform efficient geodata analysis via visualization with SQL-based interface and state-of-the-art cartographic tools in EOS Vision.
Terms and Definitions
Accuracy refers to the closeness of a measured value to a standard or known value.
Attribute – a data item associated with an individual object (record) in a spatial database. Attributes may be explicit, in which case they are typically stored as one or more fields in tables linked to a set of objects, or they may be implicit (sometimes referred to as intrinsic), being either stored but hidden or computed as and when required (e.g. polyline length, polygon centroid).
Dataset – a collection of individual data units that contains geometry and attributes, is organized in a specific way and accessed by a specific access method based on the data set organization; the main essence to represent vector data in EOS Vision.
Editor – the main workspace that represents a data-driven map, which is able to be managed with styling options, sql interface, widgets and data analysis algorithms.
Geocoding – a process of converting a physical address description into geographic coordinates. These coordinates include the longitude and latitude of the searched location or address.
Geodata – data containing geometry
Geodata analysis – the process of inspecting, cleansing , transforming, and modeling geospatial data aimed to extract meaningful info, gain insights and support decision-making.
Geometry – a set of information related to objects spatial layout
Line – a one-dimensional geometry type, described by a sequence of points defining its shape between two endpoints. It is usually used to represent roads, rivers, and other linear features.
Map – the main essence to process, visualize and analyze data in EOS Vision
Map layer – the mechanism used to display geographic dataset. Each layer references a dataset and specifies how that dataset is displayed.
Point – a zero-dimensional geometry type, described by two coordinates. It is most commonly used to represent non adjacent features, discrete data points and abstract points. Point feature could represent other geometry data at a smaller scale to reduce clutter by simplifying data locations.
Polygon – a two-dimensional geometry type, described by a sequence of points defining its exterior bounding ring and 0 or more interior rings. It is commonly used to represent parcels of land, water bodies, and other features that have a spatial extent.
PostGIS functions – a set of algorithms implemented in PostGIS (extension for PostgreSQL) for working with spatial data.
Precision refers to the closeness of two or more measurements to each other.
Product USP – a unique selling proposition of the product.
Qualitative data – a specific attributive information about qualities; information that can’t actually be measured.
Quantitative data – a specific attributive information about quantities; that is, information that can be measured and written down with numbers.
Raster data – the simplest form of data, also known as grid data, where each cell contains a value, represents the fourth type of feature: surfaces. Raster data is cell-based and this data category also includes aerial and satellite imagery.
SQL interface – an interface that allows to query the database tables from browser.
Vector data – data that are comprised of lines or arcs, defined by beginning and end points, which meet at nodes. The locations of these nodes and the topological structureare usually stored explicitly. Features are defined by their boundaries only and curved lines are represented as a series of connecting arcs. Vector storage involves the storage of explicit topology, which raises overheads, however it only stores those points which define a feature and all space outside these features is “nonexistent”.
Visualization – a set of classic techniques that provides visualization of spatial data; a combination of cartographic tools used for maps stylization, data analysis outcomes and widgets.