Monitoring Linear Disturbance Footprint Based on Dense Time Series Landsat Imagery
Bibliographic record
Abstract
Mapping linear disturbances, including pipelines, roads, and seismic lines created by resource exploration, traditionally relies on very high-resolution remote sensing data, which usually limits results to small operational areas. With increased availability of low-cost medium-resolution satellite data, complete information of linear disturbances may be monitored and reconstructed from processing time series images from more than 30 years archival data. In this study, we propose a novel approach to incorporate spectral, spatial, and temporal information for mapping and characterizing linear disturbances based on time series Landsat imagery. The mapping process involves 4 steps: line detection based on a multiscale directional template, line updating based on reappearance frequency, line connection using the Hough transform, and linear disturbance characterization. The proposed method was tested and evaluated over 4 sites in Alberta, Canada, with various linear densities for detecting and reconstructing linear disturbances from 1984–2013 using time series Landsat imagery. The results obtained by processing time series Landsat imagery have shown improved accuracy in detecting linear disturbances over that from single or multiple Landsat images. It is concluded that the strategy of integrating information from time series imagery has the potential to lead to improved operational mapping of linear disturbances.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".