Post by Brent George on Jul 24, 2024 20:40:49 GMT
HIGH-QUALITY LIDAR DATA PROCESSING SERVICES VS. AUTOMATIC PROCESSING WITH AI
lidarnews.com
18-July-2024
In today’s constantly advancing world, LiDAR technology is reaching a performance level that allows for results with high point densities, a large number of echoes recorded per pulse, great precision, and a high capacity to distinguish different objects. Simultaneously, LiDAR sensors have become more affordable, lighter, more precise, and versatile. This evolution has opened opportunities for companies worldwide to enter the data capture business by incorporating this cutting-edge technology, whether from aerial vehicles (manned or unmanned), automobiles, backpacks, or even handheld devices.
Hardware and software have evolved alongside sensors, allowing the processing and editing of tens of millions of points on a standard CAD, as well as online real-time visualization of data on remote servers or in the cloud, something unthinkable not long ago. We now have access to an increasing amount of higher quality geospatial data, while other parameters such as update frequency and available data types are also expanding.
With the advent of LiDAR sensors installed on increasingly affordable drones, the market has seen a shift from a few specialized companies to the emergence of hundreds of new players. However, the ease of investing in data capture hardware does not always guarantee the transformation of that data into valuable products. Activities such as processing and classifying LiDAR data still require significant effort and can become a bottleneck in the production chain. On the other hand, the availability of geospatial data has solidified previously emerging applications and facilitated the emergence of new ones.
Thus, in addition to products like digital elevation models, contour lines, orthophotos, and mosaics, the market demands others such as 3D vector mapping, customized distance reports, simulation of power line positions under different environmental conditions, 3D models, digital twins, etc. Exploiting these data requires efficient systems for storage, quality control, and publication on geoportals, as well as the ability to analyze them to obtain complex derivative products in increasingly shorter times.
Increases in computing power have enabled the application of algorithms for more massive and efficient data processing. The use of AI models with their learning and pattern recognition capabilities is facilitating the automation of tasks.
[Read the full article for the full details....]
lidarnews.com
18-July-2024
In today’s constantly advancing world, LiDAR technology is reaching a performance level that allows for results with high point densities, a large number of echoes recorded per pulse, great precision, and a high capacity to distinguish different objects. Simultaneously, LiDAR sensors have become more affordable, lighter, more precise, and versatile. This evolution has opened opportunities for companies worldwide to enter the data capture business by incorporating this cutting-edge technology, whether from aerial vehicles (manned or unmanned), automobiles, backpacks, or even handheld devices.
Hardware and software have evolved alongside sensors, allowing the processing and editing of tens of millions of points on a standard CAD, as well as online real-time visualization of data on remote servers or in the cloud, something unthinkable not long ago. We now have access to an increasing amount of higher quality geospatial data, while other parameters such as update frequency and available data types are also expanding.
With the advent of LiDAR sensors installed on increasingly affordable drones, the market has seen a shift from a few specialized companies to the emergence of hundreds of new players. However, the ease of investing in data capture hardware does not always guarantee the transformation of that data into valuable products. Activities such as processing and classifying LiDAR data still require significant effort and can become a bottleneck in the production chain. On the other hand, the availability of geospatial data has solidified previously emerging applications and facilitated the emergence of new ones.
Thus, in addition to products like digital elevation models, contour lines, orthophotos, and mosaics, the market demands others such as 3D vector mapping, customized distance reports, simulation of power line positions under different environmental conditions, 3D models, digital twins, etc. Exploiting these data requires efficient systems for storage, quality control, and publication on geoportals, as well as the ability to analyze them to obtain complex derivative products in increasingly shorter times.
Increases in computing power have enabled the application of algorithms for more massive and efficient data processing. The use of AI models with their learning and pattern recognition capabilities is facilitating the automation of tasks.
[Read the full article for the full details....]