Comparing the Spatial Accuracy of Digital Surface Models from Four Unoccupied Aerial Systems: Photogrammetry Versus LiDAR
Bibliographic record
Abstract
The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".