Multi-layer perceptron - Markov chain based geospatial analysis of land use and land cover change: A case study of Stoney Creek Watershed, BC, Canada
Bibliographic record
Abstract
This thesis study analyzed the land use and land cover (LULC) changes in Stoney Creek Watershed, BC, Canada using the combination of remote sensing, GIS and modeling approaches. The Object-Based Image Analysis (OBIA) tool in PCI Geomatica 2017 software was applied to generate unsupervised classification LULC maps using Landsat TM and OLI images of the years 1986, 1999 and 2016. Various band ratio were computed to improve different classification results. Esri ArcMap 10.5 was used to produce all the LULC maps for subsequent modeling. A modeling method using Multi-layer perceptron (MLP) neural network and Markov Chain (MC) was performed to predict LULC changes in 2026, using hard and soft prediction results. The outcomes of this study could provide valuable information of LULC patterns and dynamics for supporting both environmental and economic development in this area.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".