Measuring Building Height Using Point Cloud Data Derived from Unmanned Aerial System Imagery in an Undergraduate Geospatial Science Course
Bibliographic record
Abstract
The use of Unmanned Aerial Systems (UAS), also known as drones is increasing in geospatial science curricula within the United States. Within the Arthur Temple College of Forestry and Agriculture (ATCOFA) at Stephen F. Austin State University, Texas, seniors in the geospatial science program complete capstone projects to evaluate current geospatial technology to investigate complex ecological, social and environmental issues. Under the umbrella of a student initiated and designed senior project, students designed a study to estimate height of buildings with UAS data incorporating UAS data, LP360 and ArcScene programs, and Pictometry web-based interface. Results from a statistical analysis of the data confirm that geospatial science height estimation techniques can provide accurate estimates of height remotely. The independence of the students completing the project with UAS data for LP360 and ArcScene estimations, and utilizing Pictometry as an on-onscreen measuring tool, point to the need to integrate remote sensing, statistical analysis and synthesis of data into undergraduate geospatial science curricula. This reinforces the hands-on learning approach within ATCOFA and provides guidance to integrate the use of UAS in natural resource education.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".