Unsupervised Superpixel-Driven Parcel Segmentation of Remote Sensing Images Using Graph Convolutional Network
Bibliographic record
Abstract
Accurate parcel segmentation of remote sensing images plays an important role in ensuring various downstream tasks. Traditionally, parcel segmentation is based on supervised learning using precise parcel-level ground truth information, which is difficult to obtain. In this paper, we propose an end-to-end unsupervised Graph Convolutional Network (GCN)-based framework for superpixel-driven parcel segmentation of remote sensing images. The key component is a novel graph-based superpixel aggregation model, which effectively learns superpixels’ latent affinities and better aggregates similar ones in spatial and spectral spaces. We construct a multi-temporal multi-location testing dataset using Sentinel-2 images and the ground truth annotations in four different regions. Extensive experiments are conducted to demonstrate the efficacy and robustness of our proposed model. The best performance is achieved by our model compared with the competing methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".