Data

Companies have terabytes of data that Asseti transforms into competitive advantage.

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.

Collect Data

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.

Use existing datasets to understand the future

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.

Data of all types

Vector

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

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 data

Vector filetypes

LiDAR point cloud of electricity infrastructure, power lines

Linear assets including roads, power lines and oil pipelines are ideal for LiDAR mapping

Vector mapping has numerous possible filetypes, Asseti supports a growing number

Raster

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.

Method of data capture

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.

Temporal context

Coordinate systems

File size, raster compression & zoom

Raster file types

Asseti delivers value from evidence of change over time

Asseti uses raw and compressesd files top optimise experience

Multi-temporal

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.

GIS temporal data integration

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.

Best-practice temporal data management

Asseti enables easy review of change over time

Asseti temporal slider to assist review of change over time

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