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

arXiv: https://arxiv.org/abs/2103.01695 · HTML: https://mlsanity.com/paper/2103.01695/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 66 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Generating Sequences With Recurrent Neural Networks](https://mlsanity.com/paper/1308.0850/) (2013)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series](https://mlsanity.com/paper/1811.10166/) (2018)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [Classification of Time-Series Images Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1710.00886/) (2017)
- [Forecasting Economics and Financial Time Series: ARIMA vs. LSTM](https://mlsanity.com/paper/1803.06386/) (2018)
- [Lagged correlation-based deep learning for directional trend change prediction in financial time series](https://mlsanity.com/paper/1811.11287/) (2018)

## Built on by (most cited first, 13 of 13)

- [A review of machine learning in processing remote sensing data for mineral exploration](https://mlsanity.com/paper/2103.07678/) (2021)
- [Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images](https://mlsanity.com/paper/2105.08199/) (2021)
- [Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping](https://mlsanity.com/paper/2305.00640/) (2023)
- [A Deep Learning-based Approach for Real-time Facemask Detection](https://mlsanity.com/paper/2110.08732/) (2021)
- [Securing the Classification of COVID-19 in Chest X-ray Images: A Privacy-Preserving Deep Learning Approach](https://mlsanity.com/paper/2203.07728/) (2022)
- [Enhancing Cognitive Workload Classification Using Integrated LSTM Layers and CNNs for fNIRS Data Analysis](https://mlsanity.com/paper/2407.15901/) (2024)
- [Leveraging Artificial Intelligence Techniques for Smart Palm Tree Detection: A Decade Systematic Review](https://mlsanity.com/paper/2209.05282/) (2022)
- [GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide](https://mlsanity.com/paper/2105.13426/) (2021)
- [Predicting cognitive load in immersive driving scenarios with a hybrid CNN-RNN model](https://mlsanity.com/paper/2408.06350/) (2024)
- [Enabling Advanced Land Cover Analytics: An Integrated Data Extraction Pipeline for Predictive Modeling with the Dynamic World Dataset](https://mlsanity.com/paper/2410.09135/) (2024)
- [Cross-Modal Urban Sensing: Evaluating Sound-Vision Alignment Across Street-Level and Aerial Imagery](https://mlsanity.com/paper/2506.03388/) (2025)
- [Remote sensing data imputation using deep learning for multispectral imagery](https://mlsanity.com/paper/2605.24003/) (2026)
- [A comparative and critical study of EEGNet for fNIRS-driven cognitive load classification](https://mlsanity.com/paper/2606.16160/) (2026)

## Abstract (arXiv)

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.