A Novel CNN-LSTM-based Approach to Predict Urban Expansion

TL;DR

This paper introduces a new CNN-LSTM-based method for predicting urban expansion from time-series satellite images, demonstrating improved accuracy over existing approaches in major Saudi cities.

Contribution

The paper presents a novel two-step approach combining semantic segmentation with CNN-LSTM models for urban expansion prediction from satellite data.

Findings

Improved performance in urban expansion prediction metrics.

Effective use of CNN-LSTM for temporal feature learning.

Superior results compared to state-of-the-art methods.

Abstract

Time-series remote sensing data offer a rich source of information that can be used in a wide range of applications, from monitoring changes in land cover to surveilling crops, coastal changes, flood risk assessment, and urban sprawl. This paper addresses the challenge of using time-series satellite images to predict urban expansion. Building upon previous work, we propose a novel two-step approach based on semantic image segmentation in order to predict urban expansion. The first step aims to extract information about urban regions at different time scales and prepare them for use in the training step. The second step combines Convolutional Neural Networks (CNN) with Long Short Term Memory (LSTM) methods in order to learn temporal features and thus predict urban expansion. In this paper, experimental results are conducted using several multi-date satellite images representing the three largest cities in Saudi Arabia, namely: Riyadh, Jeddah, and Dammam. We empirically evaluated our proposed technique, and examined its results by comparing them with state-of-the-art approaches. Following this evaluation, we determined that our results reveal improved performance for the new-coupled CNN-LSTM approach, particularly in terms of assessments based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.