A Multitask CNN-Transformer Network for Semantic Change Detection From Bitemporal Remote Sensing Images
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Bibliographic record
Abstract
Bitemporal remote sensing (RS) semantic change detection (SCD) involves discerning and categorizing changes in the same geographical area across two RS images taken at different times. High-performance SCD approaches typically address this task using multitask networks that simultaneously handle binary change detection (BCD) and semantic segmentation (SS). Despite significant advancements in SCD research, constructing a multitask network that fully explores the correlation between BCD and SS remains challenging. To address this, we propose a novel approach called the multitask CNN-transformer network (MCTNet), tailored for SCD using bitemporal RS images. Our Siamese network simultaneously tackles SS and BCD via three subnetworks: two for SS and one for BCD. The methodology begins with a multiscale convolutional neural network (CNN) extracting local features from input images, and converting them into tokens. A Transformer module with an encoder-decoder architecture then captures long-range dependencies among these visual tokens. The extracted features are subsequently passed to multitask heads, generating predicted outputs. To ensure that the BCD results remain consistent regardless of the order of images in the input pair, we introduce spatiotemporal feature learning (SFL), enabling the acquisition of temporal-symmetric representations for BCD. Extensive experimental validation on the WHU-CD, SECOND, and HRSCD datasets demonstrates the effectiveness and efficiency of MCTNet for both SS and BCD tasks. The source code for this article will be published on GitHub in the future <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/kangziwen1/MCTNet</uri>.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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