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How can you manage LiDAR data for large GIS projects?

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LiDAR, or Light Detection and Ranging, is a remote sensing technique that uses laser pulses to measure the distance and elevation of objects on the earth's surface. LiDAR data can provide high-resolution and accurate information for various GIS applications, such as terrain mapping, vegetation analysis, hydrology, and urban planning. However, LiDAR data also poses some challenges for managing large GIS projects, such as data volume, storage, processing, and visualization. In this article, you will learn some tips and best practices for handling LiDAR data effectively and efficiently.

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1 Understand your data needs

Before beginning to work with LiDAR data, it is essential to have a thorough understanding of the kind of data you need, the quantity of data, and the level of detail and quality required. This will help you to plan your data acquisition, storage, and processing strategies, as well as prevent unnecessary costs and delays. For instance, you should consider the purpose and scope of your project: its main objectives and outputs, how LiDAR data will be used to accomplish them, and the size and complexity of the area of interest. Additionally, you must determine the type and format of LiDAR data needed - discrete or waveform? Point cloud or raster? LAS or LAZ? - as well as how many returns and attributes are necessary. Additionally, you must consider the quality and accuracy of LiDAR data: specifications and standards for your LiDAR data; spatial resolution and density; vertical and horizontal accuracy and precision; and ways to assess and validate the quality.

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Sachin Kumar Bobbili

GIS Analyst
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LiDAR data has very detailed information with great precision, but it is somewhat complex for beginners to understand. This data is important for commercial use in infrastructure and development projects. It can provide sub-meter accuracy with the best resolution. According to the project's specifications and other requirements, such as horizontal and vertical accuracy, the data must be selected. Due to these advantages, it is more costly than other remote sensing data acquisition techniques and also requires different software to analyze the data.

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Anuj Gupta

Principal Product Manager, Autopilot Map @ Mapbox | ex- Cruise, Amazon, Civilmaps (acquired by Luminar)
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For accuracy, do consider both relative accuracy (accuracy of relative distance between lidar points vs ground truth) and absolute accuracy (accuracy in earth reference frame)

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2 Organize your data structure

Once you have acquired or obtained your LiDAR data, you need to organize it in a way that facilitates data management and analysis. This means creating a data structure that reflects your project's workflow and hierarchy, as well as following naming and metadata conventions that make your data easy to identify and access. For example, you may want to divide your data into subfolders based on geographic regions, data sources, data types, or processing stages. Additionally, files and folders should be named using descriptive and informative labels that include relevant information, such as location, date, sensor, resolution, or version. Furthermore, it is important to document your data using metadata standards like FGDC or ISO that provide essential information about your data's origin, content, quality, format, coordinate system, and usage.

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3 Optimize your data storage

LiDAR data can be very large and consume a lot of storage space, especially if you have multiple files or layers of data. To optimize your data storage, you should consider compressing your data with lossless compression methods like LAZ or ZLAS, which reduce the file size without sacrificing any information. Additionally, you can clip your data using spatial or attribute filters such as bounding boxes or classes to remove any unnecessary or redundant data outside your area of interest. Lastly, indexing your data with spatial or attribute indexes like spatialite or geopackage can enhance the performance and speed of your queries and operations.

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Anuj Gupta

Principal Product Manager, Autopilot Map @ Mapbox | ex- Cruise, Amazon, Civilmaps (acquired by Luminar)
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If you want to work with existing 2d tools and focus on ground objects, the size of point cloud can be reduced by projecting a 2d intensity image of all ground points to create a 2 d image.

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4 Streamline your data processing

LiDAR data can be complex and require a lot of processing to extract meaningful information for your GIS project. To streamline the data processing, you may want to use batch processing tools, such as ArcGIS Model Builder or QGIS Graphical Modeler, which allow you to create and run workflows that perform multiple tasks and operations on your data. Additionally, scripting languages such as Python or R can be used to write and execute code that performs customized and advanced functions and calculations on your data. Finally, cloud computing platforms such as Google Earth Engine or AWS offer access and processing of large amounts of data in a scalable and distributed way.

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Anuj Gupta

Principal Product Manager, Autopilot Map @ Mapbox | ex- Cruise, Amazon, Civilmaps (acquired by Luminar)
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Make sure to align (loop close) the trips to themself for relative alignment, as well as to a ground control point or other geo-referencing methodology (aerial imagery, satellite imagery, aerial lidar) to get required absolute accuracy.

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5 Enhance your data visualization

LiDAR data can be detailed and complex, making it difficult to visualize and communicate effectively. To enhance your data visualization, you may want to use symbology and color schemes that represent the variation and distribution of your data values. Additionally, 3D visualization tools, such as ArcGIS Scene Viewer or QGIS 3D Map View, can help create and explore realistic 3D models of the data. Web mapping tools, like ArcGIS Online or Leaflet, can also be used to create and share dynamic and interactive web maps and applications.

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Anuj Gupta

Principal Product Manager, Autopilot Map @ Mapbox | ex- Cruise, Amazon, Civilmaps (acquired by Luminar)
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To optimize work on lidar data, especially with offshore operations, create 2d projections of lidar data to use existing 2d tools and optimize tool & operator performance. In addition, with accurate calibration data, features can be extracted in camera space and projected into lidar space to improve operator efficiency and accuracy.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Jared Horne

ALDOT Enterprise Information Analyst Sr at Alabama Department of Transportation
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Something that should not be underestimated is the weight of processing large scale mobile lidar data. This is not as big a concern with aerial lidar, but mobile lidar operations of thousands of miles benefit from extra attention toward ensuring a smooth processing supply chain. I have found it useful to treat large mobile operations as a factory assembly line and stagger processing routines. It is worthwhile exploring distributed processing options if handling terabytes at a time.

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Anuj Gupta

Principal Product Manager, Autopilot Map @ Mapbox | ex- Cruise, Amazon, Civilmaps (acquired by Luminar)
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Make sure you have the required tooling in place to ingest lidar data. It is especially important when ordering external paid survey data acquisition.

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How can you manage LiDAR data for large GIS projects?

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6

1 Understand your data needs

2 Organize your data structure

3 Optimize your data storage

4 Streamline your data processing

5 Enhance your data visualization

6 Here’s what else to consider

Geographic Information Systems (GIS)

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How can you manage LiDAR data for large GIS projects?

1

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4

5

6

1 Understand your data needs

2 Organize your data structure

3 Optimize your data storage

4 Streamline your data processing

5 Enhance your data visualization

6 Here’s what else to consider

Geographic Information Systems (GIS)

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