Those in possession of a LiDAR dataset will know that there are many applications for your data – whether it’s creating digital elevation models, identifying natural or built landscape features, determining vegetation characteristics or checking infrastructure health. What you also need to know is that not all LiDAR data sets are created equal. It’s not enough to simply get a unit in the air to collect some data. Without determining your objectives for the LiDAR you will collect, appropriate planning prior to LiDAR capture, use of good quality processes during capture and appropriate post capture processing and checking, your LiDAR capture exercise can be both costly and end with a data set that is unsuitable for your proposed application.
This 4 part blog series from Anditi’s chief software architect and LiDAR data set diagnostics guru Patrick Poissant and Head of Business Operations and quality queen Catherine Pepper gives you some tips on common pitfalls to avoid in the capture of LiDAR data to put yourself ahead of the curve and ensure that your dataset is fit for purpose. Over the next month or so we’ll cover aspects like:
Today’s post focuses on planning your project to ensure you’re setting yourself up to succeed.
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Pretty much everyone has heard the old (and probably overused) adage “those who fail to plan plan to fail”. And when it comes to LiDAR capture programs we unfortunately find this to be true. There is nothing worse than working on a project only to find that the data has been captured at an insufficient resolution, with the wrong instrument or hasn’t covered the entire area of interest to get the results our customers are looking for. That’s why we recommend that you think about the following points right at the very start of your project, before you’ve even started thinking about getting planes in the air.
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Start by thinking about what you want to achieve in your project and the eventual outcomes your LiDAR capture should contribute towards. This should include asking yourself questions like:
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LiDAR and other remotely sensed data is becoming increasingly available in Australia. Before starting down the pathway of a capture program, make sure you’ve identified what data sources already exist. Your state government land information services provider might be able to help with information within your own state, or Anditi’s spatial data experts can give you information on what’s already available across the nation.
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Talking to an expert in spatial data analytics about the project objectives can really help hone your understanding of the project and the type of data that needs to be collected. Just half an hour on the phone with someone can save many hours down the track, so doing this sooner rather than later really helps.
Getting the data specification right is a crucial component of project planning. Ensuring the specification matches the project objectives will save time and money and ensure that any subsequent analytics have the best chance of yielding great results. Think about things like:
These are just a few of the points from our experience to cover in planning your project to ensure that your final LiDAR point cloud is fit for its intended use. Let us know about some of your experiences in project planning to improve LiDAR results by commenting below.
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If your LiDAR point cloud is giving you trouble, or you want to talk about planning an upcoming project, we’d love to talk – feel free to drop us a line or give us a call.
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