Machine Learning Analysis of Land Use and Vegetation Changes in Dhaka District, Bangladesh (2019-2024)
A study published on arXiv (2608.12001) applies remote sensing and machine learning to analyze land use and vegetation changes in Dhaka District, Bangladesh, from 2019 to 2024. Using Sentinel-2 MSI and Landsat 8 satellite imagery, researchers classified land cover types and computed spectral indices including NDVI, NDBI, and NDWI. Supervised classifiers—Decision Tree, K-Nearest Neighbors (KNN), and Random Forest—were implemented within Google Earth Engine using labeled geospatial training points. Accuracy was assessed via confusion matrices. The research addresses rapid urbanization's impact on environmental conditions, aiming to inform urban planning and ecological sustainability. The study highlights the effectiveness of integrating remote sensing data with machine learning for systematic environmental monitoring in rapidly urbanizing regions.
Key facts
- Study analyzes land use and vegetation changes in Dhaka District, Bangladesh between 2019 and 2024.
- Uses remote sensing data from Sentinel-2 MSI and Landsat 8 satellites.
- Computes spectral indices: NDVI, NDBI, and NDWI.
- Applies supervised machine learning classifiers: Decision Tree, K-Nearest Neighbors (KNN), and Random Forest.
- Analysis conducted within Google Earth Engine using labeled geospatial training points.
- Accuracy assessments performed using confusion matrices.
- Motivated by rapid urbanization and its environmental impacts.
- Aims to support informed urban planning and ecological sustainability.
Entities
Institutions
- arXiv
Locations
- Dhaka District
- Bangladesh