2026-10-06T07:10:43Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33834586 2026-09-28T06:01:27Z category_26629 category_25795 category_25852 category_26635 item_type_3 month_year_09_2026

Multi-Temporal UAV LiDAR Dataset of Mountainous Terrain Acquired with DJI Zenmuse L1 Patryk Wróblewski (24666396) Geospatial information systems and geospatial data modelling Geoscience data visualisation Geodesy Photogrammetry and remote sensing UAV LiDAR DJI Zenmuse L1 DJI Matrice 300 Mountaineous terrain Airborne Laser Scanning (ALS) Multi-temporal data multi-temporal data analyses Dataset Overview This dataset contains bi-temporal UAV LiDAR point clouds covering a 90-hectare mountainous area located in Tylicz, Poland. 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.Data Acquisition Details<ul><li>Platform: DJI Matrice 300 RTK</li><li>Sensor: DJI Zenmuse L1</li><li>Epoch 1: April 9, 2025 (Flight altitude: 80 m AGL)</li><li>Epoch 2: April 14, 2026 (Flight altitude: 90 m AGL)</li><li>Flight Parameters: Speed 7.5 m/s, side overlap 20%, Terrain Follow enabled, non-repetitive scanning mode.</li></ul>Key Challenges & Included Tools 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).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.Potential Applications This dataset is highly suitable for researchers and spatial data engineers focusing on:<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>Literature:<ul><li>N. Pfeifer, G. Mandlburger, J. Otepka, W. Karel: <a href="http://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="http://www.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. XXXVIII, Part 3/W8 (2009), ISSN: 1682-1750; 55 - 60.</li><li>J. Otepka, G. Mandlburger, W. Karel: <a href="http://dx.doi.org/10.5194/isprsannals-I-3-153-2012" target="_blank">The OPALS Data Manager - Efficient Data Management for Processing Large Airborne Laser Scanning Projects;</a> in: ISPRS Annals, Comm. III, Volume 1-3 (2012), ISSN: 2194-9042; 153 - 159.</li></ul> 2026-09-28T06:01:27Z Dataset Dataset 10.6084/m9.figshare.33834586.v2 https://figshare.com/articles/dataset/_b_Multi-Temporal_UAV_LiDAR_Dataset_of_Mountainous_Terrain_Acquired_with_DJI_Zenmuse_L1_b_/33834586 CC BY 4.0