- [ONLINE] IN33D-VR8804: Spatiotemporal Dynamics and Machine Learning-Based Hotspot Classification of Artificial Light at Night in Nigeria (2014-2024): An AI-Assisted Geospatial Analysis
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Samuel Olatunde Ajoniloju, Beihang University (First Author, Presenting Author)
Wuraola King, Beihang University
Puspendu Biswas Paul, National Oceanographic and Maritime Institute
Kareem Yusuf, University of Lisbon
Abdul-Sobbur Maltiti Alhassan, Beihang University
Using satellite images to track human activity, urbanization, and economic growth, this study investigates the change of artificial light at night (ALAN) in Nigeria between 2014 and 2024. Nigeria during the night appeared up to 126% brighter suggesting widespread and accelerated growth in electricity access across the country, but also notable urban development. This growth isn't uniform, with 32 bright hotspots mostly in key economic and urban centers like Lagos and Kano, and many rural areas remain dark. By employing sophisticated tools within the Google Earth Engine, we characterized these patterns and used machine learning to begin to understand why they occur. Nighttime light patterns were mainly affected by distance to cities, temperature and population densities. This illustrates Nigeria's impressive growth yet demonstrates regional disparity and possible environmental repercussions, including disruption to wildlife and ecosystems (sea turtles and migratory birds). This can provide important suggestions to urban planning and energy policies attuning for the developmental growth of Nigeria while maintaining a balance between development and natural resource utilization by preserving the environmental well-being state to ensure development sustainability.
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