Search Results

The following example loads a DataFrame for a provided parameter product in the workflow: # Import voxel farm client and workflow lambda from voxelfarm import voxelfarmclient from voxelfarm import workflow_lambda # Obtain the workflow_api interface wor...

The following code performs a search in the Time Series database: data_frame = vf.search_time_series( spatial_id="Z602_404_812_408_ALH_5", object_id="CRUSHER3", property="DRY_TONNES", # An array of two ISO8601 dates timespan=["2017-01...

This example shows how to retrieve event data from Time Series for an array of time series identifier: timeseries_ids = [ { "spatialID": "B101_408_807_408_BAH_14", "objectID": "CRUSHER3", "property": "DRY_TONNES", }, ...

The following example creates a report by running a lambda Report function: result = voxelfarm_workflow.create_report(vf, request, 'pending_reports', 'Survey Volume Difference', 'lambdas/surface-volume-difference.py', region, 4, { 'previo...

The following example shows how to create a view using the workflow interface: voxelfarm_workflow.create_view(vf, request, 'Composite Difference', None, 'lambdas/view-terrain-composite-difference.py', { 'previous_surface_id' : f'{previous_terrain_id}',...

Before getting started, ensure you have: A VoxelSpace account (the free tier allows up to 5 GB of uploaded data and includes limited compute hours). Data in supported  file formats as outlined in the upload wizards, including common point-cloud, mesh, bloc...

Follow these steps to create your first project: 1. Navigate to platform.voxelspace.com and log in with your Microsoft, Google identity or your work email.2. On the catalog page, click New Project3. Provide a project name and description4. Select your time zo...

VoxelSpace's spatial lambdas are serverless functions that execute custom computations on your volumetricdata. They run near your data on highly parallel infrastructure, allowing you to process trillions of voxels inminutes. This guide explains what spatial la...

Spatial lambdas are stateless cloud functions written in languages such as Python or .NET. They take voxel gridsas input, perform a computation, and return a result or a modified dataset. Examples include: Volume calculations and statistical summaries Filt...

Inside your project, click Add Object → Raw Data and choose the type of dataset you want to import: Point Cloud Upload LiDAR or photogrammetry point clouds Supported formats: .las, .laz, .txt, .xyz, or compressed .zip files Provide an object name and opt...

To make data queryable and visualizable, convert raw objects into indexed datasets or voxel grids: Indexed Points Converts a raw point cloud into a queryable point-cloud dataset. Select your source point cloud and click Create.  Indexed Mesh Similar to Ind...

Use a spatial lambda when you need to: Compute metrics such as total volume tonnage average density, or other summarystatistics Filter voxels based on property thresholds (e.g., density > 2.5) or remove empty voxels Resample data to a coarser or finer res...

Once you have indexed datasets, explore them in the interactive 3D viewer: Creating a View Select New View from the project or choose View from an indexed object Enter a name and description for your view Working with the View Editor Click Add Objec...

VoxelSpace offers powerful capabilities beyond basic visualization: Spatial Calculations Run serverless spatial lambdas to: Compute volumes across datasets Filter data based on specific criteria Derive metrics across trillions of voxels  Temporal Tra...

Spatial lambdas are typically composed of three main components: 1. Input Definition Specify the dataset(s) and region of interest (bounding box or selection) to process. 2. Compute Function Write code that iterates through voxels and applies your logic. F...

Follow these steps to execute a spatial lambda: Step 1:Prepare Your Dataset Ensure your data is processed into an indexed dataset or voxel grid. Lambdas operate on indexed datasets ratherthan raw uploads. Step 2:Select or Upload a Lambda Navigate to your d...

Performance Optimization Limit the region: Restrict the lambda to the smallest area necessary to reduce compute costand time Efficient algorithms: Use vectorized operations where possible for better performance Memory management: Be mindful of memory usage ...

Here's a more sophisticated lambda that performs statistical analysis: def statistical_analysis_lambda(context, voxel_reader, voxel_writer): grades = [] volumes = [] for voxel in voxel_reader: grade = voxel.properties.get('grade',0.0) if...

Performance Problems Large memory usage: Process voxels in batches rather than loading all into memory Slow execution: Optimize algorithms and reduce unnecessary computations Timeout errors: Break large jobs into smaller regions    Data Issues Miss...