Mask R-CNN based automated identification and extraction of oil well sites
Bibliographic record
Abstract
Fine-scale land disturbances due to mining development modify the land surface cover and have cumulative detrimental impacts on the environment. Understanding the distribution of fine-scale land disturbances related to mining activities, such as oil well sites, in mining regions is of vital importance to sustainable mining development. For efficient mapping, automated identification and extraction of the oil well sites using high-resolution satellite images are required. In this work, we proposed the Oil Well Site extraction (OWS) Mask R-CNN based on the original Mask R-CNN (Region-based Convolutional Neural Networks), to accurately extract well sites using multi-sensor remote sensing images. For improvement of mapping efficiency, two modifications were made to Mask R-CNN: (1) replacing the backbone of Mask R-CNN with D-LinkNet, and (2) adding a semantic segmentation branch to Mask R-CNN to force the whole network to focus on the relationship between line objects and oil well sites. As imagery data were from multiple sensors (RapidEye 2/3 and WorldView 3), a pre-trained Residual Channel Attention Network (RCAN) was applied to super-resolve the images with different resolutions. Several key spatial features, such as nearby roads and area size, have also been used in the oil well site mapping process. The experimental results indicate that our OWS Mask R-CNN considerably improves the average precision (AP) and the F1 score of Mask R-CNN from 51.26% and 25.7% to 60.93% and 61.59%, respectively.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".