Utilizing Low Level Videography and GIS to Rethink Traditional Phase I Environmental Site Assessments
Bibliographic record
Abstract
The method used for traditional “Phase I” Environmental Site Assessments (ESA’s) has required staff to physically walk the rights-of-way (ROW). In order to compete the ESA in a more timely and cost-effective manner than traditional techniques allowed, URS Corporation (URS) contracted LinearVision (LV) to fly the utility ROW and collect low-level, airborne, geo-referenced videography, complemented with geo-referenced still-imagery to enable URS to complete the Phase I ESA data analysis in their office. The data provided enabled URS’s analysts the ability to access all ROW points of interest in their Geographic Information System (GIS), and “fly” the line with oblique and downward perspectives from their computer screen. The digital video could be sped up or slowed down allowing URS the ability to review each frame for careful, detailed analysis, and identify potential recognized environmental conditions (RECs) and encroachment upon the ROW. The high-resolution still-imagery provided URS the ability to zoom into the potential REC for a closer inspection of site features, vegetation, and surrounding land use. URS created a database of potential RECs and areas of interest along with a simple user interface as a deliverable to the client, which allows the user to link directly to specific video frames and high-resolution photographs for their own review and analysis. The cost of the airborne data capture and processing was substantially less than a traditional Phase I ESA. Furthermore, the video and high-resolution images provided for a more comprehensive analysis tool, which can be reviewed by multiple analysts and catalogued for future reference by third parties. An additional benefit is that the high-resolution photographs can be imported into AutoCAD or other software for development of maps and figures. Overall, the net result of low-level GIS integrated videography is enhanced quality of data and a 50% reduction in total cost for the ROW project as compared to traditional Phase I ESA methodology.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".