# Discovering Effective Policies for Land-Use Planning with Neuroevolution

arXiv: https://arxiv.org/abs/2311.12304 · HTML: https://mlsanity.com/paper/2311.12304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel](https://mlsanity.com/paper/1906.00588/) (2019)
- [Towards assessing agricultural land suitability with causal machine learning](https://mlsanity.com/paper/2204.12956/) (2022)
- [Effective Reinforcement Learning through Evolutionary Surrogate-Assisted Prescription](https://mlsanity.com/paper/2002.05368/) (2020)
- [EVOTER: Evolution of Transparent Explainable Rule-sets](https://mlsanity.com/paper/2204.10438/) (2022)

## Built on by (most cited first, 1 of 1)

- [Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity](https://mlsanity.com/paper/2606.20817/) (2026)

## Abstract (arXiv)

How areas of land are allocated for different uses, such as forests, urban areas, and agriculture, has a large effect on the terrestrial carbon balance, and therefore climate change. Based on available historical data on land-use changes and a simulation of the associated carbon emissions and removals, a surrogate model can be learned that makes it possible to evaluate the different options available to decision-makers efficiently. An evolutionary search process can then be used to discover effective land-use policies for specific locations. Such a system was built on the Project Resilience platform and evaluated with the Land-Use Harmonization dataset LUH2 and the bookkeeping model BLUE. It generates Pareto fronts that trade off carbon impact and amount of land-use change customized to different locations, thus providing a proof-of-concept tool that is potentially useful for land-use planning.