The use of remote sensing to identify habitats on a large-scale linear infrastructure project
Bibliographic record
Abstract
Abstract The majority of large-scale infrastructure projects are required to undertake an Environmental Impact Assessment (EIA). This process enables environmental specialists to identify what resources may be impacted by the proposed development in order to influence the design and provide mitigation where required. Part of the process is establishing the pre-existing or baseline conditions. For ecology, this is established by undertaking an ecological phase 1 survey, which is an important preliminary stage and often a pre-requisite for further detailed study. The process for undertaking an ecological phase 1 survey is in guidance produced by the Joint Nature Conservation Committee and reflected in the High Speed Two (HS2) Field Survey Methods and Standards. The guidance, published in 2010, states that limited value can be derived from using remote sensing in preparing for such a survey. However, since 2010 the quality of information that can be obtained from remote sensing has improved significantly. HS2 Ltd has utilised these improvements to drive efficiencies in the way in which ecological phase 1 surveys have been undertaken. HS2 Ltd has used algorithms and remote sensing data to automatically pre-classify the entire Phase 2b route, this has enabled the ecological phase 1 habitat surveyors to attend site with a pre-populated ecological phase 1 habitat map. This allows the surveyor to focus on ground-truthing, target noting, and giving more attention to potential ecological constraints highlighted by the normalised difference vegetation index data. Time has been saved on having to scribe and delineate habitats in the field. Pre-digitised maps have enabled tablets to be used for ground-truthing, with efficient data-handling and GIS data processing when back in the office. The positive outcomes from this approach have included efficiency savings during data collection compared to traditional methods, health and safety benefits on site, high level information obtained for sites where physical access was not possible, time savings on data processing, and greater precision in highlighting ecological constraints on sites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".