Integrated Use of Aerial Photographs and LiDAR Images for Landslide and Soil Erosion Analysis: A Case Study of Wakamow Valley, Moose Jaw, Canada
Bibliographic record
Abstract
Urban parks and open spaces offer a unique setting that can play a vital role in improving health and quality of life in cities and towns, making cities more attractive places to live and work, and connecting residents to nature. Degradation of park facilities caused by natural processes or recreational activities requires continuous monitoring for efficient maintenance and management. Identification and continuous monitoring of areas prone to natural hazards such as landslides within an urban park are particularly important for public safety. Traditional techniques for identification and monitoring of such areas involving field surveys, being costly and time-consuming, cannot be used on a regular basis. This research explored the integrated use of aerial photographs and point cloud LiDAR data for identification of areas prone to landslide and soil erosion zones in an urban park and a conservation area known as Wakamow Valley, Moose Jaw, Saskatchewan, Canada. This study used the point cloud LiDAR of 2014 to develop a Digital Elevation Model (DEM) of the area. The accuracy of the DEM was validated through a series of well-distributed ground control points collected through a survey grade handheld GPS device. The areas prone to potential landslides and soil erosion were identified using slope analysis techniques. A typical criterion of areas having a slope greater than 35° was used for classification of potential hazardous zones. Geospatial information including land-cover, land-use, and trail system was extracted from a 2014 aerial photograph to create a base map. It has been estimated that 5.3 km along the banks of the Moose Jaw River and 8 km along the cliff of the canyon-shaped Wakamow Valley are under a possible threat of soil erosion and landslides. This portion of the valley was classified as high-risk for possible landslides and soil erosion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".