Optimizing Lidar Processing with Customized Workflows

Why does lidar data processing matter? 

Lidar has emerged as a cornerstone technology for a wide array of applications, ranging from topographic mapping and urban planning to digital terrain models and environmental monitoring. As lidar datasets grow in complexity and volume, the need for efficient and customized processing workflows becomes more important. 

In this blog, we explore how companies can optimize lidar processing through tailored workflows which helps maximize efficiency and accuracy. 

Read more: https://gno-sys.com/stages-of-a-lidar-survey/ 

What are the challenges in lidar processing?

  1. Data Volume: lidar datasets are enormous, taxing computational resources and storage capacity. 
  2. Complexity: Point clouds contain noise, outliers, and artifacts that must be filtered out for accurate analysis. 
  3. Application-Specific Requirements: Different applications necessitate different levels of data processing and analysis, requiring customized workflows. 
  4. Limited accessibility: Not being accessible by the whole team, especially those who do not know python coding. 

What is the current standard way of processing lidar?

Many companies handle their processes in an ad-hoc manner, often resorting to scripts or searching for tools that may not seamlessly integrate into their workflow or operate in parallel. Consequently, they expend significant manual effort to navigate their data workflows, leading to inconsistent results and potential errors. 

Because they may lack the necessary expertise, they face the dilemma of either learning Python programming or hiring external scriptwriters, which may not yield optimal solutions. 

Moreover, many companies struggle with data organization, relying on different storage solutions like hard drives or network locations, often resulting in chaotic data management and hindered collaboration between departments. 

What strategies are there to effectively customize lidar processing workflows for companies, and how does the utilization of cloud-based processing systems contribute to this process?

Developing customized lidar processing workflows tailored to specific needs is crucial for addressing challenges, and companies can achieve this effectively by scripting, automating, and designing new workflows using languages like Python or MATLAB. This allows for efficient data processing and analysis. Additionally, utilizing cloud-based processing systems provides scalable computing resources and specialized tools for handling large datasets and executing complex algorithms without extensive infrastructure. Cloud environments also facilitate collaboration and accessibility, enabling stakeholders to contribute from anywhere. Combining customized workflows with cloud-based processing enhances efficiency, accuracy, and adaptability to project requirements. 

At GNO-SYS, when we work with a client, we begin by clearly defining the objectives of the lidar processing workflow. It is important to know the end goals, whether it is terrain modelling, feature extraction, or change detection.  Our team at GNO-SYS relies on innovative solutions, such as our cloud-based and scalable processing system GNOde, which streamlines the entire workflow. We have taken the time to build out and automate the required steps in a cloud environment. The cloud, along with GNOde enables the seamless assembly of a complete processing workflow for lidar data from start to finish. 

How does the workflow begin? How does parallel processing enhance efficiency in the workflow?

Automating lidar data processing entails establishing a systematic workflow to execute tasks like importing raw scans, filtering noise, and classifying points without manual intervention. This involves defining processing steps, developing scripts or utilizing software tools, and monitoring performance for accuracy and reliability. Once optimized, the automated pipeline efficiently handles large volumes of data, reducing processing time and human effort while ensuring consistent and reliable results. This scalable approach enhances productivity for tasks like land use mapping, urban planning, and infrastructure monitoring. 

The workflows at GNO-SYS work like this: The process initiates when data is dropped into an S3 bucket, triggering the workflow. Initially, the zip file is decompressed, and the data is extracted into the cloud storage in an output folder. The subsequent processes run in parallel, which enhances efficiency. For instance, tasks like point cloud classification and the addition of raw data to STAC happen simultaneously since they are independent processes. Further down the workflow, another set of parallel tasks occurs, contributing to overall processing efficiency. 

What capabilities does GNOde offer for workflow automation and task management, and how does it empower customers to tailor their data processing workflows according to their needs?

Workflows can also be configured to automatically initiate processes and notify stakeholders upon completion. Features such as pausing processes for approval or quality control checks can be incorporated to add human interaction where necessary. GNOde, our cloud-based data management system, serves as a repository for these tasks and jobs. 

Examples of such tasks include common operations like “create DSM“, “create hillshade” or “void filling.” The objective is to compile a comprehensive library of these smaller tasks and allow customers to construct or customize workflows using GNOde. This flexibility allows customers to efficiently manage and process their data according to their specific requirements.  

How do the capabilities of cloud's batch processing empower customers to tailor their workflows?

The cloud offers batch processing capabilities, which are essential utilities owned by the platform. These utilities can be leveraged by customers for their own processes, or customized and created specifically for them. Owned batch processes provide flexibility, enabling customization with tools not readily available. Additionally, a library of these utilities and substep functions exists, facilitating modular workflows. Workflows can call other workflows, allowing projects to be broken down into manageable components. This modular approach enables the creation of simple, reusable workflows tailored to different customers’ needs. 

How does having a single, streamlined workflow contribute to more efficient data processing and management?

Optimizing workflows offers several advantages. Firstly, repeatability and time-saving are key benefits. With a well-defined setup, workflows perform the exact same tasks consistently, saving time and ensuring reliability. Secondly, workflows are user-friendly due to their visual nature. Users can easily understand and manipulate workflow components, enhancing ease of use. Additionally, workflows empower individuals without coding experience to build processes. By leveraging pre-established tools and Python coding, users can focus on learning how to utilize tools within the workflow rather than mastering coding techniques. 

Our solution streamlines this process by centralizing results in an online data store, facilitating easy search, download, and sharing capabilities. This ensures efficient data management and promotes seamless collaboration across teams. 

Case Study

For a recent project, we collaborated with a client utilizing the STAC/GNOde catalog. This company specializes in terrestrial lidar scanning within urban environments, and they were looking for significant scalability for large projects. Their focus was on ease of use, aiming to reduce person-hours required for processing and management. Their primary goal was to streamline their processing workflow, access the results efficiently, and share the processed data with users across multiple geographic locations.  

With the workflow we devised, they could simply drop their zip files into the system and retrieve their processed files later, without the need to write any code. This workflow enabled them to focus solely on the output without concerning themselves with the underlying processing mechanisms. 

Their objective was to process lidar data and ensure accessibility of results to other team members without requiring them to understand the intricacies of the processing pipeline. By granting access to the catalog, team members could utilize the processed data for their work without being burdened by technical details. Centralizing all data within a STAC catalogue significantly enhanced data organization, addressing a common challenge faced by aerospace and geospatial companies. 

Can machine learning be leveraged to improve data classification?

The integration of machine learning capabilities will allow for enhanced classification of data, such as identifying different types of buildings, leveraging spatial data to augment this process. With our expertise in geospatial data processing, we have the capacity to develop tailored workflows based on specific requirements. By understanding the desired inputs and outputs, we can design workflows that optimize efficiency and functionality, allowing our clients to focus on their objectives while we manage the technical aspects behind the scenes. 

In conclusion, optimizing lidar processing with customized workflows is essential for aerospace companies to reach the full potential of this technology. As lidar continues to play a crucial role in aerospace applications, mastering customized processing workflows will be key to staying ahead in the industry. Engaging a specialized geospatial company like ours ensures access to a blend of geospatial and software development proficiency. This unique synergy allows for the seamless integration of solutions tailored precisely to your requirements, all while safeguarding your intellectual property rights. By opting for our services, you avoid the expense of expensive software licenses. Our holistic approach not only tackles your immediate needs but also uncovers efficiencies that might otherwise go unnoticed.