Asseti is a powerful data engine, with the capacity to consume and make sense of vast geospatial datasets. Spatially referenced vector and raster datasets are the most commonly added.
Companies have stored status and action data records for decades, and big data AI/ML-powered systems can interrogate that history to accurately recommend future action.
Asseti’s digital asset management platform specialises in collecting extraordinarily large data sets and making them useful to asset managers, asset network managers and the teams they work with. Asseti processes trillions of raw data points which are stored and analysed to derive actionable insights.
Insights are served to asset managers based on their properties, historical and current conditions, and projects for future likelihoods based on the experience at their sites and by other similar facilities. Asseti makes past datasets continuously useful to inform future actions.
Asseti is a type-agnostic data consumer and accepts dozens of formats. New file formats are added routinely and we encourage you to reach out with specific type requirements.
Vector data has geospatial coordinates, and the combination of points determine if the dataset represents a point, arc or polygonal information. Vectors in asset management reference locations in which change should be monitored, such as a telegraph pole, power line corridor, building boundary or solar farm envelope.
Point clouds are extremely rich datasets in which each point carries an X, Y, and Z geometric spatial coordinate. Points are generated by signals such as from LiDAR, with millions of points per second quickly building an extremely accurate and complete picture or map.
Companies with point cloud datasets will likely have experienced issues in storing and using the data easily, let alone layering further meaningful information over it to draw insights. Asseti is designed and configured to serve this need.
Linear assets include infrastructure like electricity networks, roads and oil pipelines. Linear assets require management for safety and ongoing integrity – for example, the interconnected subsidence impact of weather events or environmental intrusion on the integrity of high voltage power line corridors.
LiDAR survey and management over time through Asseti can assure service uptime and optimises asset value.
There is an extensive list of vector file types used in the GID community. Some – such as DLG – as falling out of favour whereas Esri Shapefiles and JSON formats are enduringly popular.
Asseti currently accepts major vector formats and will progressively build out all formats required through our agile development approach.
Raster data is presented in a grid of pixels carrying values, and can be 2D or 3D. Raster data typically refers to imagery, aerial data and large continuous spatial datasets.
Raster datasets are invaluable in the asset management world, as they depict defects and change over time – for example, photogrammetry can be used to determine stockpile inventory, and continuous imagery can be used to identify roofing defects and vegetation danger to infrastructure. Asseti is experienced in making raster data enduringly valuable to the data owner.
GIS ‘data capture’ refers to the method of creating datasets with necessary attributes across location, time and features. Raster data is primarily image-based and captured by a camera or sensor that parses grids of pixels.
Asseti has a partner network to assist in data capture if required. The most common methods are aerial data capture from manned or unmanned vehicles (that is, planes and helicopters versus drones), as well as low orbit capture from satellite, ground-vehicle mounted imaging and fixed point imaging. Asseti is optimised to consume all types of raster data and provide insights to asset managers.
Raster dataset files include multiple attributes, with timestamp being one of the most vital. Geospatial information without a timestamp is not useful to assess change over time and consequently Asseti will not take unstamped data.
Asseti has been designed and built for asset managers to use their current status data more effectively, and through that to build a thorough and insightful picture of the past with which to extrapolate a vision of the future.
Asseti incorporates the mathematical demands and scaleable architecture to consume data that adheres to the main coordinate systems. Geographical coordinate systems use a 3D vision of the earth’s spherical surface to define points as measured from the earth’s centre as latitude and longitude, whereas projected coordinates form a 2D representation of the earth and use mathematical formulae to relate spherical 3D coordinates to the 2D projection. As a native cloud application, Asseti has effectively infinite storage and computing power to consume and process coordinated raster datasets.
Raster GIS files are extremely large and sit in datasets of hundreds or thousands of images. Asseti consumes and uses the original files, and processes compressed versions for faster intuitive platform use without loss of data resolution.
In practice, the original large files are only be seen in their original raw format by Asseti's processing engine and authorised users that zoom to that granular level. Users are typically served optimised raster images and thumbnails when zoomed to an asset level and tiny stitched images at a higher level, with individual images virtually withdrawn at a macro level. In this way, Asseti prioritises speed and user experience while serving insights identified at a full-resolution.
There is an extensive set of raster file types accepted in the GIS software sector, which are detailed in this resource page. Note that all single-layer image filetypes, such as JPG, PNG, RAW, TIF are also accepted by Asseti so long as timestamp data is appended. Asseti also natively consumes raster GIS image collections compressed within ZIP files.
Asseti accepts most raster formats and will progressively build out additional formats as required by partners, within our agile development approach.
Multi-temporal data is a subset of datasets that incorporates two or more datasets of the same focal area or element. Multi-temporal datasets do not require even time-lapse intervals and can carry independent observations.
Stationary assets such as buildings and pipelines derive change over time insights from multi-temporal datasets, which asset network managers use to plan maintenance and repair operations. Asseti is optimised to understand and serve insights based on condition change over time.
Time is built into all spatial platforms, including Asseti. Supported temporal datatypes include feature layers, mosaic datasets, netCDF layers (Network Common Data Form), tables, catalogues, and data feeds. Asseti infers time from data attributes and stores that data contextually as associated with the asset and site hierarchy.
Connect with Asseti to discuss specific filetype requirements.
The most important recommendation for GIS temporal data integrity is to use reputable data capture technologies or service providers, and leave temporal data intact. Where some configuration is required temporal data should be configured within a field designed to accept time intelligence. and an ISO or ISO adjacent format should be used. It is strongly recommended that universal or standardised timestamp be used, to avoid inaccuracy based on daylight savings.
Asseti is optimised for time queries, using an intuitive slider and having time attributes indexed for faster query performance.