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Using Lambda Layers with USGS LiDAR AWS Public Dataset

Author: Howard Butler
Date: 01/18/2019


Point clouds are fast becoming a primary geospatial datatype. Much like typical raster and vector data, point clouds provide measurement and inference of geophysical properties. While they are not quite raster images and not quite abstract geography, point clouds represent individual, discrete measurements in space, and they are commonly created using LiDAR scanners, coincidence matched imagery, sonar, and radar.

AWS provides one of the most convenient cloud environments to process massive point cloud data. Products such as Lambda, Batch, and Elastic Graphics can allow you to take advantage of the scaling infrastructure that AWS can provide without taking on as much management burden. This post will describe how to use Lambda in combination with the new USGS LiDAR Public Dataset to quickly produce an elevation model directly from LiDAR data using the PDAL open source software.

USGS Public Dataset

The USGS 3D Elevation Program (3DEP) organizes the national acquisition and processing of much of the LiDAR elevation content in the United States. Recently, USGS begun uploading final 3DEP point cloud data into a s3://usgs-lidar Requester Pays bucket with 1.4 million tiles ASPRS LAS tiles compressed using the LASzip compression encoding in the us-west-2 region. Hobu, Inc. and the USACE Cold Regions Research and Engineering Laboratory collaborated with the AWS Public Datasets team to organize this data as Entwine Point Tile (EPT) resources. You can find out more about this project on the AWS Public Dataset page.


The open source Entwine software takes point cloud data and organize them into single resources called Entwine Point Tiles (EPT). Like their raster- and vector- tile brethren, EPT provides a simple, deterministic octree tree structure for clients to access data. EPT also provides implementation convenience in the form of JSON-based metadata and LASzip-based compressed encoding. EPT is a lossless data structure that allows software to control the request pace and resolution of data while being able to predict resource consumption. This makes it a suitable data structure for supporting both visualization and exploitation scenarios.


PDAL is open source software for translating, extracting, filtering, and exploiting geospatial point cloud data. Its latest version includes a reader for EPT data called readers.ept, and we will use it in combination with Lamda to read a small section of point cloud data, use PDAL's filtering operations to remove vegetation, and output a digital terrain model for a region.

AWS recently introduced the Lambda Layer concept to allow developers to stack and version Lambda functionality. For this exercise, we are taking advantage of a public PDAL Lambda Layer the PDAL development team maintains that provides all of the basic library functionality we will need. Atop that, we will use a Python Lambda function to call PDAL on for our requested area.


Our scenario is to create a Python Lambda function that calls pdal pipeline over a user-specified bounding area of Entwine Point Tile data

The U.S. Geological Survey (USGS) has been leading the 3D Elevation Program (3DEP) in various forms since XXXX. 3DEP funds the acquisition of LiDAR data over the United States, and the USGS makes raw point cloud data and processed elevation content available for download via FTP servers – much like the same distribution scenario the Landsat program operates.

Recently, USGS has begun uploading final 3DEP point cloud data into a s3://usgs-lidar Requester Pays bucket with 1.4 million tiles ASPRS LAS tiles compressed using the LASzip compression encoding in the us-west-2 region. USGS has saved us a heavy lift by pushing these data to the cloud, but they are not organized in a way that we can conveniently utilize the point cloud data without significant processing.

Information extraction from massive point clouds is a challenging topic, and developers reach for cloud computing scenarios to achieve performance with dynamic workloads. Because point cloud data are spatially partitioned in a straightforward manner, GPU-based computing and highly parallel processing are a frequently applied computing approach.

The two most common point cloud data capture scenarios are actively scanned LiDAR and passively processed coincident imagery. Each have use scenarios that make them more attractive, but they both have the property of providing overwhelming data volume. Over the past ten years or so, open source software to compress, organize, and extract information from these point clouds has been developed. Users can now combine these tools to take advantage of these data types.

Cost pressure on LiDAR systems, especially driven by the autonomous vehicle industry, is regularizing data that was once boutique government information. Governments, on the other hand, have a history of collecting massive LiDAR collections and then placing them on the shelf due to their challenging nature. To date, most of these collections have not been fully utilized beyond simple elevation modeling. An opportunity to do even more exists, but storage, processing, and network infrastructure are needed to capitalize upon it.

Open Source Software

As mentioned, open source software has matured to process, extract, and exploit point cloud data. Open source tools are frequently combined with cloud computing to build data processing workflows, and the tools for point cloud data are mature enough to meet the challenge. Some typical point cloud processing challenges and complimentary software include:

The opportunity cloud infrastructure provides point cloud processing scenarios is significant. LiDAR data quickly meet any definition of Big Data, with the simplest being a collection larger than a single typical laptop can hold. Aerial LiDAR collections over municipalities quickly meet that definition.