Simulating urban expansion with interpretable cycle recurrent neural networks

Abstract

Recent advances in deep learning have brought new opportunities for analyzing land dynamics, and Recurrent Neural Networks (RNNs) presented great potential in predicting land-use and land-cover (LULC) changes by learning the transition rules from time series data. However, implementing RNNs for LULC prediction can be challenging due to the relatively short sequence length of multi-temporal LULC data, as well as a general lack of interpretability of deep learning models. To address these issues, we introduce a novel deep learning-based framework tailored for forecasting LULC changes. The proposed framework uniquely implements a cycle-consistent learning scheme on RNNs to enhance their capability of representation learning based on time-series LULC data. Moreover, a local surrogate approach is adopted to interpret the results of predicted instances. We tested the method in a LULC prediction task based on time-series Landsat data of Shenzhen, China. The experiment results indicate that the cycle-consistent learning scheme can bring substantial performance gains to RNN methods in terms of processing short-length sequence data. Also, the tests of interpretation methods confirmed the feasibility and effectiveness of adopting local surrogate models for identifying the influence of predictor variables on predicted urban expansion instances.