Oil Spill Detection Based on Multiscale Multidimensional Residual CNN for Optical Remote Sensing Imagery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Oil Spill (OS), as one of the main pollutions in the ocean, is a serious threat to the marine environment. Thus, timely and accurate OS Detection (OSD) is necessary for ocean management. In this regard, Remote Sensing (RS) plays a key role due to multiple advantages over large and remote ocean environments. In this study, a new OSD framework based on a deep learning algorithm was developed for optical RS imagery. The proposed method was based on a multi-Scale multi-dimensional residual kernel Convolution Neural Network (CNN). The proposed method investigated the deep features by the two-dimensional (2D) multi-scale residual blocks and, then, utilized them at one-dimensional (1D) multi-scale residual blocks. In this study, Landsat-5 satellite imagery acquired over the Gulf of Mexico was applied to evaluate the performance of the proposed method. The Overall Accuracy (OA) of the proposed method was more than 95%, and the Miss Detection (MD) and False Alarm (FA) rates were less than 5%, indicating its high potential for OSD. Moreover, it was observed that the proposed method had better performance compared to other OSD algorithms that were investigated in this study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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