An efficient built-up land expansion model using a modified U-Net

ABSTRACT This paper introduces an improved convolutional neural network based on the conventional U-Net for simulating built-up land expansion. The proposed method hires a pixel-wise semantic segmentation approach considering the spatial drivers affecting urbanization as data cubes. Independent variables including altitude, slope, and distance from barren, crop, greenery, roads, and urban areas for 1998, 2008, and 2018 were considered as covariates for the simulation of built-up land expansion in Tehran and Karaj regions in Iran. The proposed method was compared with the random forest (RF) algorithm as the baseline model. Evaluation using the area under the total operating characteristic indicated the superiority of our modified U-Net (0.87) over the RF (0.82) algorithm. Furthermore, evaluation using the percent correct metric indicated that our proposed model is capable of learning neighborhood effects effectively leading to simulate built-up land expansion accurately, independent from applying a cellular automata (CA) model. Therefore, the modified U-Net independent from the CA which can consider the neighborhood effects is recommended for the simulation of built-up land expansion precisely.

• Publication year

  2022

• Venue

  International Journal of Digital Earth

• Publication date

  2022-02-04

• Fields of study

  Geography, Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1080/17538947.2021.2017035
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Simulation of Dynamic Urban Expansion under Ecological Constraints Using a Long Short Term Memory Network Model and Cellular Automata

  2021cited by this paper
• Integrating a Forward Feature Selection algorithm, Random Forest, and Cellular Automata to extrapolate urban growth in the Tehran-Karaj Region of Iran

  2021influential reference
• Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs' Classifiers Incorporating Artificial Neural Network Using Landsat Data

  2021influential reference
• A Novel CNN-LSTM-based Approach to Predict Urban Expansion

  2021cited by this paper
• Urban sprawl and its impact on sustainable urban development: a combination of remote sensing and social media data

  2020cited by this paper
• Assessing environmental impacts of urban growth using remote sensing

  2020cited by this paper
• Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

  2020influential reference
• Big spatial data for urban and environmental sustainability

  2020cited by this paper
• A Novel Multi-Attention Driven System for Multi-Label Remote Sensing Image Classification

  2019cited by this paper
• Detecting global urban expansion over the last three decades using a fully convolutional network

  2019cited by this paper
• Hyperspectral Image Classification

  2019cited by this paper
• Challenges and opportunities for the development of MEGACITIES

  2018cited by this paper
• Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation

  2018cited by this paper
• Modeling land use change using Cellular Automata and Artificial Neural Network: The case of Chunati Wildlife Sanctuary, Bangladesh

  2018cited by this paper
• Mining transition rules of cellular automata for simulating urban expansion by using the deep learning techniques

  2018cited by this paper
• Analysis of the dynamics of land use change and its prediction based on the integration of remotely sensed data and CA-Markov model, in the upstream Citarum Watershed, West Java, Indonesia

  2018cited by this paper
• Integrating Cellular Automata with the Deep Belief Network for Simulating Urban Growth

  2017cited by this paper
• Urban expansion: a geo-spatial approach for temporal monitoring of loss of agricultural land

  2017cited by this paper
• A segment derived patch-based logistic cellular automata for urban growth modeling with heuristic rules

  2017cited by this paper
• Understanding of a convolutional neural network

  2017cited by this paper
• Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth

  2017cited by this paper
• Going Deeper With Contextual CNN for Hyperspectral Image Classification

  2016cited by this paper
• Land-use classification

  2016cited by this paper
• Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks

  2016cited by this paper
• U-Net: Convolutional Networks for Biomedical Image Segmentation

  2015cited by this paper
• Deep Convolutional Neural Networks for Hyperspectral Image Classification

  2015cited by this paper
• Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

  2015cited by this paper
• Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model

  2015cited by this paper
• Land Use Classification in Remote Sensing Images by Convolutional Neural Networks

  2015cited by this paper
• Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world

  2014cited by this paper
• Modeling multiple land use changes using ANN, CART and MARS: Comparing tradeoffs in goodness of fit and explanatory power of data mining tools

  2014cited by this paper
• The total operating characteristic to measure diagnostic ability for multiple thresholds

  2014cited by this paper
• Deep learning in neural networks: An overview

  2014cited by this paper
• Challenges and Opportunities

  2013cited by this paper
• Random Forests

  2012cited by this paper
• Land-use and land-cover change : local processes and global impacts

  2010cited by this paper
• Modeling urban growth using a variable grid cellular automaton

  2009cited by this paper
• Comparing the input, output, and validation maps for several models of land change

  2008cited by this paper
• Modeling urban growth in Atlanta using logistic regression

  2007cited by this paper
• From The Cover:Land Change Science Special Feature: The emergence of land change science for global environmental change and sustainability

  2007cited by this paper
• An introduction to ROC analysis

  2006cited by this paper
• Assessing cellular automata model behaviour using a sensitivity analysis approach

  2006cited by this paper
• Determinants of Land-Use Change Patterns in the Netherlands

  2004cited by this paper
• A method to analyse neighbourhood characteristics of land use patterns

  2004cited by this paper
• Impact of urbanization and land-use change on climate

  2003cited by this paper
• Using neural networks and GIS to forecast land use changes: a Land Transformation Model

  2002cited by this paper
• A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China

  2001cited by this paper
• THE APPLICATION OF THE LAND TRANSFORMATION , GROUNDWATER FLOW AND SOLUTE TRANSPORT MODELS FOR MICHIGAN ’ S GRAND TRAVERSE BAY WATERSHED

  2001cited by this paper
• Are agricultural land-use models able to predict changes in land-use intensity?

  2000cited by this paper
• Urban Systems as Cellular Automata

  1997cited by this paper
• Effects of forest fragmentation on neotropical fauna: current research and data availability

  1995cited by this paper
• Cellular Worlds: A Framework for Modeling Micro—Macro Dynamics

  1985cited by this paper
• Signal detection theory and ROC analysis

  1975cited by this paper
• The emergence of land change science for global environmental change and sustainability

  year unknowncited by this paper

• Containing urban sprawl in China: A cross-city evaluation of urban development boundaries using U-Net deep learning

  2025cites this paper
• Best Practices for Applying and Interpreting the Total Operating Characteristic

  2025cites this paper
• Advancing urban expansion modeling with a hybrid TRANSGAN deep learning approach

  2025cites this paper
• A predictive model for spatial and temporal evolution of tidal flats based on STIAU, improved vision transformer and cellular automata

  2025cites this paper
• Deep Learning Analysis of Urban Growth Boundaries: An Evaluation of Effectiveness in Mitigating Urban Sprawl in China

  2024cites this paper
• A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping

  2024cites this paper
• A Novel Deep Learning Prospective Urban Growth Model Applied to Multispectral Satellite Data over the City of Córdoba, Argentina

  2024cites this paper
• A novel full-resolution convolutional neural network for urban-fringe-rural identification: A case study of urban agglomeration region

  2024cites this paper
• A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas

  2023cites this paper
• Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

  2023cites this paper
• Automatic extraction of built-up areas in Chinese urban agglomerations based on the deep learning method using NTL data

  2023cites this paper
• Enhancing a convolutional neural network model for land subsidence susceptibility mapping using hybrid meta-heuristic algorithms

  2023cites this paper
• A moving window-based spatial assessment method for dynamic urban growth simulations

  2022cites this paper
• Multi-scenario based urban growth modeling and prediction using earth observation datasets towards urban policy improvement

  2022cites this paper
• Modeling urban expansion by integrating a convolutional neural network and a recurrent neural network

  2022cites this paper