Evaluating long short-term memory networks for modeling land cover change
Bibliographic record
Abstract
Land cover change (LCC) can be viewed as dynamic complex systems which require relevant relationships to be encoded when represented within various modeling approaches.Recurrent Neural Networks (RNNs), specifically the Long Short-Term Memory (LSTM) variant, belong to a category of Deep Learning (DL) approaches best suited for sequential and timeseries data analysis, thus suitable for representing LCC.The primary objective of this study is to examine the capacity and effectiveness of LSTM networks for forecasting LCC given varying geospatial input datasets with feature impurities.Using synthetic and MODIS land cover datasets for British Columbia, Canada, results demonstrate the sensitivity of LSTM models to varying geospatial input dataset characteristics.Geospatial datasets with finer temporal resolutions and increased timesteps yielded favourable results while coarser temporal resolutions and fewer timesteps were affiliated with less successful outcomes.This thesis research contributes to the advancement of automated, data-driven DL methodologies for forecasting LCC.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".