Multi-Temporal UAV LiDAR Dataset of Mountainous Terrain Acquired with DJI Zenmuse L1

The data was collected to evaluate the capabilities, geometric limitations, and processing bottlenecks of low-cost UAV LiDAR systems for sub-decimeter micro-relief change detection and geomorphological analysis.</p><p dir="ltr"><b>Data Acquisition Details</b></p><ul><li><b>Platform:</b> DJI Matrice 300 RTK</li><li><b>Sensor:</b> DJI Zenmuse L1</li><li><b>Epoch 1:</b> April 9, 2025 (Flight altitude: 80 m AGL)</li><li><b>Epoch 2:</b> April 14, 2026 (Flight altitude: 90 m AGL)</li><li><b>Flight Parameters:</b> Speed 7.5 m/s, side overlap 20%, Terrain Follow enabled, non-repetitive scanning mode.</li></ul><p dir="ltr"><b>Key Challenges & Included Tools</b> A significant challenge in processing data from closed-ecosystem ("black-box") commercial software like DJI Terra is the lack of proper <code>Point Source ID</code> assignment and restricted access to the Scanner's Own Coordinate System (SOCS).

This limits the ability to perform rigorous kinematic strip adjustments in professional external environments (e.g., OPALS).</p><p dir="ltr">To address this, the dataset includes a custom Python script (<code>dji_terra_strip_extractor.py</code>). This tool parses high-precision SBET trajectory logs, utilizes a Straightness Index (SI), and dynamically extracts individual flight lines to assign the correct <code>Point Source ID</code> to the point clouds, enabling further advanced preprocessing and ICP (Iterative Closest Point) block alignment.</p><p dir="ltr"><b>Potential Applications</b> This dataset is highly suitable for researchers and spatial data engineers focusing on:</p><ul><li>Algorithmic testing for UAV LiDAR point cloud co-registration and strip adjustment.</li><li>Evaluating the impact of IMU angular drift ("lever arm effect") on flight line edges in high-relief terrain.</li><li>Analyzing the influence of phenological windows and vegetation on ground classification algorithms (e.g., CSF).</li><li>Advanced 3D deformation and micro-relief analysis using distance computation algorithms like M3C2 vs. standard C2C.</li></ul><p dir="ltr"><b>Literature</b>:</p><ul><li>N. Pfeifer, G. Mandlburger, J. Otepka, W. Karel: <a href="dx.doi.org/10.1016/j.compenvurbsys.2013.11.002" target="_blank">OPALS - A framework for Airborne Laser Scanning data analysis.</a> Computers, Environment and Urban Systems, 45 (2014), 125 - 136.</li><li>G. Mandlburger, J. Otepka, W. Karel, W. Wagner, N. Pfeifer: <a href="isprs.org/proceedings/XXXVIII/3-W8/papers/55_laserscanning09.pdf" target="_blank">Orientation And Processing Of Airborne Laser Scanning Data (OPALS) - Concept And First Results Of A Comprehensive Als Software.</a> in: IAPRS, Vol.