Identifying and managing overgrown vegetation presents a significant challenge for train operating companies seeking to maintain efficient operations. Vegetation overgrowth causes communication breakdowns, uneven terrain, obstructions, and fire hazards, resulting in inconsistent operations, potential for accidents, and reduced train speeds. Sending crews to manually survey samples at crossings is inefficient, costly, and impractical at a large scale.

Light Detection and Ranging (LiDAR) technology provides a swift, precise, and economical approach to identify overgrown vegetation and streamline maintenance activities. This technology has not only transformed vegetation management, but has modernized processes ranging from autonomous vehicles to environmental conservation and emergency response.


Class 1 Railroad Company


The leading transportation company with a vast network of rail, intermodal, and rail-to-truck transload services spanning over 21,000 miles of track across 23 states in the U.S., as well as in Ontario and Quebec. Since its inception, the industry leader has remained committed to providing safe, efficient, and reliable transportation services to its customers. With a focus on innovation and leveraging cutting-edge technology, the company consistently sets new standards for quality and safety.

To ensure smooth operations and reduce safety hazards, a solution that could survey all rail crossings for vegetation overgrowth while complying with federal regulations on visibility became essential.


Vegetation growth can occur rapidly and can vary widely depending on local weather conditions, geography, and other environmental factors. The current approach of surveying random samples of crossings does not provide a comprehensive view of the vegetation growth state, which leads to potential safety risks and regulatory non-compliance.

Sending out crews to manually inspect and maintain vegetation growth at rail crossings can be dangerous, exposing personnel to hazardous conditions and accidents. It is also an expensive process that requires significant time and resources to complete.

The current approach lacks a proactive maintenance strategy, by relying on reactive measures that can lead to delays and disruptions in railway operations. Given the scale and complexity of railway networks, it was crucial to develop an innovative approach to optimize vegetation management at rail crossings.


Object Computing utilized a fresh approach to LiDAR, blending geospatial mastery, mathematical modeling, and streamlined Google Earth Engine (GEE) data to rapidly and accurately survey all crossings for overgrown brush.

The LiDAR application detected and classified vegetation elevation, measured vegetation height, and created a 3D map of the crossing area. The data collected was then analyzed to determine the level of vegetation growth and identify areas that required maintenance. Using this derived LiDAR elevation data, the application calculated which crossings were the most compliant with specified height regulations and identified crossings that required vegetation management.


By implementing LiDAR technology, transportation companies can more efficiently monitor and manage vegetation growth at rail crossings, allowing for quick and effective responses to any issues while minimizing disruptions to railway operations. This not only saves valuable resources but also ensures compliance with state regulations, improving public safety and reducing costs.

The application was built using Object Computing’s proprietary application ecosystem, Asterisms, which originated from the cross-industry need for an interactive data management experience that can outpace regulatory and industry-specific demands.

The Asterisms ecosystem offers the opportunity to achieve unprecedented levels of accuracy and speed in remote sensing-based monitoring and analysis, allowing for more effective and reliable decision-making. The framework allows Object Computing to deliver meaningful outcomes for enterprises seeking to improve sustainability, efficiency, intelligence, and revenue–in weeks, not months.


By considering scalability from the outset, Object Computing designed the LiDAR mobile app with the potential to support advanced functionalities in the future.

For instance, by integrating year-over-year LiDAR data analytics and AI methods, the app can be used to build predictive models that forecast the speed and density of vegetation growth, allowing for more proactive management practices. AI integration could also be utilized to identify specific areas that require maintenance and generate automated work orders for crews to perform.

This forward-thinking approach not only provides valuable insights into more effective vegetation management but also contributes to the development of sustainable and environmentally friendly practices.

As technology continues to evolve, the LiDAR mobile app’s scalability ensures it remains a valuable tool for railway companies, helping them to maintain compliance with state regulations, ensure public safety, and reduce disruptions to railway operations.


At Object Computing, we understand that every enterprise has unique challenges and requirements, which is why we work closely with our clients to customize our solutions to their specific needs. Our team of experienced engineers and data scientists can help you identify opportunities for improvement and develop strategies to achieve your goals. 

Contact us today to learn more about how we can help your enterprise turn rich data insights into actionable, impactful outcomes.