Urban land-cover classification based on swarm intelligence from high resolution remote sensing imagery
Bibliographic record
Abstract
Urban land-cover classification is one of the most challenging problems in pattern analysis and machine intelligence systems in remote sensing. Dense urban environment sensed by very high-resolution (VHR) optical sensors is even more challenging. Occlusions and shadows due to buildings and trees hide some objects in the scene. Despite its simplicity and usefulness, conventional classification methods have failed to have a high classification accuracy in dense urban areas. The objective of this study is to improve the quality of the land-cover classification. We propose using a Particle Swarm Optimization (PSO) based algorithm for the classification of VHR (0.1 to 1 m) remote sensing data over urban areas. This method is to discover classification rules through simulating the social behavior of animals such as the behaviours of bird flocking. The results for aerial images classification show the significance of this method. PSO-based classifier has been applied to the classification of remote sensing data in dense urban district of Kitchener-Waterloo and has achieved high predictive accuracy of 90%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".