LidarTeam: a remote sensing driven method for massive lidar data to regional DHM refined through user feedback
Year: 2025
Type: article
Abstract: This paper presents a method to obtain Digital Height Models from massive airborne lidar data processing (billions of points) over tens of thousands of km2, along with a new Geospatial User Feedback (GUF) geoservice. The method avoids large errors common in previous procedures and achieves high accuracy thanks to the synergy with other Remote Sensing (RS) data and vector/raster heuristics. Additionally, it provides per-cell metadata about algorithm decisions and reliability (10 categories each), as well as the day-month-year of the lidar pulse used (key for forest growth studies). Tests carried out on 5355 + 4163 points on buildings + forests have shown median errors of 19 and 43 cm, MAE of 64 and 98 cm, and RMSE of 157 and 164 cm. The time-series dataset is available to everyone both for download and through a geoservice following FAIR principles. Moreover, the geoservice includes GUF functionalities allowing users to comment and report issues at both the per-cell level and through areas defined by coordinates (not only at dataset level). The result of this research, LidarTeam, is a teamwork involving RS sources, producers and end users to create a piece of a futuristic library of Digital Earth products, specifically a multitemporal 3D representation of the World.
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,390
OpenAlex ID: W4415413904