ArticleScientific reports2025
Spatio-temporal analysis of urban expansion and land use dynamics using google earth engine and predictive models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Decoding peri-urban transformation through multi-scale mapping of bidirectional urbanisation.Scientific reports · 2026Article
- Sustainable EV adoption with clustering and predictive modelling for optimal charging infrastructure in the West Midlands and North East UK.Scientific reports · 2026Article
- Fine-Scale Ecological Biomonitoring in a Large, Complex Agriculturally Impacted Watershed via eDNA Metabarcoding.Molecular ecology · 2026Article
- Geospatial modeling and forecasting of urban land use change using Google Earth Engine and machine learning.PloS one · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Urban expansion and changes in land use/land cover (LULC) have intensified in recent decades due to human activity, influencing ecological and developmental landscapes. This study investigated historical and projected LULC changes and urban growth patterns in the districts of Multan and Sargodha, Pakistan, using Landsat satellite imagery, cloud computing, and predictive modelling from 1990 to 2030. The analysis of satellite images was grouped into four time periods (1990-2000, 2000-2010, 2010-2020, and 2020-2030). The Google Earth Engine cloud-based platform facilitated the classification of Landsat 5 ETM (1990, 2000, and 2010) and Landsat 8 OLI (2020) images using the Random Forest model. A simulation model integrating Cellular Automata and an Artificial Neural Network Multilayer Perceptron in the MOLUSCE plugin of QGIS was employed to forecast urban growth to 2030. The resulting maps showed consistently high accuracy levels exceeding 92% for both districts across all time periods. The analysis revealed that Multan's built-up area increased from 240.56 km
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