Analyzing Fuzzy Logic, Logistic-Decision Tree, and Neural Network Classification for Extracting Subzonal Land Uses from Remote Sensing Imagery
Bibliographic record
Abstract
Lack of elaborate land use (LU) information has forced city planners and modelers to use large aggregated zones in their models, and consequently accept undesirable approximations and errors in their analyses and planning workflow. This paper presents and compares the performances of three classification techniques developed and designed for LU extraction through a hybrid geographic information (GI)/remote sensing (RS) expert system. The hybrid system is designed to classify urban subzones, in this case, dissemination blocks (DBs), into pure LUs using very high resolution (VHR) aerial imagery and several GIS data. The LU classification techniques developed in this study, which are the core of the proposed expert system, includes Fuzzy-Decision Tree, Logistic-Decision Tree, and Artificial Neural Network (ANN). Several types of GIS data, including building footprint, street network, digital property map (DPM), and dissemination block (DB) zones from the study area, City of Fredericton, Canada, are fused into the LU classification expert systems to discover correlation/association rules related to urban LU classes. Morphological properties of a number of selected DBs, which contain different types of pure LUs, are derived from GIS/RS data. Zonal properties, including vegetation ratio, soil ratio, building ratio, street ratio, parking ratio, mean building area, mean building perimeter, mean building compactness, average height of buildings, and sum of soil and parking ratio, are used as potential indicators in the classification process. Based on the accuracy assessment, the testing results of the three classification techniques indicate that Logistic-Decision Tree has the best performance with an overall accuracy of 95.2%. Continuing development of the proposed GI/RS expert system will have an important implication to current modeling process by providing up-to-date and much more detailed land use information.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".