AI and remote sensing track Dhaka's rapid urbanization and environmental shifts

A new study published on arXiv details the use of remote sensing and machine learning to analyze land use and vegetation changes in Dhaka District, Bangladesh, between 2019 and 2024. The research employed satellite imagery and algorithms like Decision Tree, K-Nearest Neighbors, and Random Forest to track shifts in land cover. Findings revealed a significant increase in urban built-up areas (59.5%) alongside declines in vegetation (-8.46%) and water bodies (-7.77%), highlighting the environmental impact of rapid urbanization. The study emphasizes the utility of these technological tools for sustainable urban planning and policy. AI

IMPACT Provides a framework for monitoring urban development and environmental changes using AI, potentially informing policy and sustainable planning.

RANK_REASON The cluster contains a single academic paper detailing research methodology and findings. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• Bangladesh
• Decision Tree
• Dhaka District
• Google Earth Engine
• K-Nearest Neighbors (KNN)
• Landsat 8
• Md. Alamgir Hossain
• Normalized Difference Built-up Index (NDBI)
• Normalized Difference Water Index (NDWI)
• Random Forest

• paper
• other

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