Defining Transition Rules with Reinforcement Learning for Modeling Land Cover Change
Bibliographic record
Abstract
Spatio-temporal modeling provides the opportunity to simulate geographic processes of land use and land cover change (LUCC) by integrating geographic information systems (GIS) with various machine learning approaches to computing. Contemporary models are often developed using a training dataset to define a set of probabilistic transition rules that govern how a landscape changes over time. However, the use of training datasets can be problematic for spatio-temporal modeling, as they can limit the ability to incorporate system complexity and hinder the transferability of the model to different datasets. The purpose of this study is to evaluate a machine learning approach called reinforcement learning (RL) for defining transition rules for GIS-based models of land cover change due to natural resource extraction. Specifically, RL is evaluated based on its potential for constructing models independent of training datasets that can handle different levels of complexity and be transferred across different spatial extents. An RL model for Land Cover Change (RL-LCC) is developed for considering economic and ecological goals involved in natural resource management, and implemented using a hypothetical forest management scenario. Simulation results reveal that agents in the RL-LCC model are able to develop transition rules from their experience in their landscape in a variety of simulation scenarios that allow them to achieve their goals. This study demonstrates the benefits of integrating RL and GIS in order to address important issues of space, time and complexity.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".