Semantic Change Detection with Constrained Dual-Head Convolutional Neural Network Architecture for Oil/Gas Well Site Monitoring
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Bibliographic record
Abstract
High-resolution mapping of land disturbance and reclamation is important for assessing the cumulative environmental effects of oil/gas production. The growing availability of high-resolution satellite imagery, combined with recent advances in deep learning, offers a desirable solution for detecting land surface changes on disturbed land. In this study, we constructed the Alberta oil/gas wells semantic change detection (SCD) data set in Alberta, Canada, based on high-resolution satellite imagery from WorldView-2 and SPOT-6. The data set consists of 328 pairs of bitemporal images (512 × 512 pixels at 1.5-m resolution), along with corresponding semantic change maps, binary change maps, and land cover maps. In addition, we proposed a constrained dual-head convolutional neural network (CNN) framework that jointly learns semantic change and binary change tasks. Specifically, two segmentation heads are designed—one for semantic changes and one for binary changes—and are explicitly connected through a cosine similarity loss that enforces consistency between the two tasks. Taking High-Resolution Net (HRNet)-v2 as the backbone, our model was pretrained on the large-scale SEmantic Change detectiON Data Set (SECOND) and fine-tuned on our developed data set. Comparative experiments with BiSRNet, HGINet, and SCanNet demonstrate that our approach achieves superior performance, with the highest mean intersection over union (mIoU) (79.47%) and separated Kappa (SeK) (28.40%) after fine-tuning. Incorporating land cover maps as additional supervision further boosts results, with our approach reaching an mIoU of 80.05% and a SeK of 29.71%. These findings highlight the effectiveness of the proposed constrained dual-head CNN architecture and the benefit of leveraging land cover information for advancing SCD in remote sensing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it