By Kingsley Alumona
Data scientist and co-founder of Geosoft Global Innovation, Ugochukwu Charles Akajiaku, has stated that a novel combination of artificial intelligence (AI) and geospatial technology is the way forward to tackling climate change in Nigeria and Africa at large.
Akajiaku, a data scientist at Golden Viosam Nigeria, stated this on Monday while commenting on how African governments and industry stakeholders can manage the climate change challenges facing the continent.
He noted that as someone who grew up in the Niger Delta region of Nigeria, he witnessed firsthand the impact of environmental degradation, flooding, and poor infrastructural development on the side of the government and the oil companies in the region.
“I was always curious about how science and technology could help us solve these problems,” he said.
According to him, that curiosity led him to study Geology, earn a master’s in Geography and Environmental Management, and later another master’s in International Project Management in the United Kingdom.
He said that his field and research work on environmental and climate, especially as they relate to exploring AI and big data in predicting and managing them, has led him to the conclusion that AI, big data, and geospatial technology are the panacea to climate change problems.
His co-authored peer-reviewed publications including ‘Shoreline erosion and accretion analysis of the Orashi River (2025)’ and ‘Using ML and GIS to monitor sandbars along the River Niger (2025), among others, and some of his expert opinions in notable Nigerian newspapers, he said, have contributed to how some of these environmental issues could be addressed.
“These studies explore how Africa can localise technology to protect coastlines, ecosystems, and inland waterways. They are examples of how science and open innovation can provide tools to help communities prepare for natural disasters rather than react to them,” he said.
According to Akajiaku, at Golden Viosam, he has built scalable AI-powered environmental tools and is also the mind behind open-source solutions like Flood Mapping Toolkit, which he said is helping cities and researchers better plan for climate-related disasters.
He added that his big break came when he combined his passion for geospatial data with machine learning to build a flood-prediction model for communities along River Niger.
“The model used satellite imagery, weather data, and elevation models to forecast high-risk areas before disasters hit. That project helped to cut down emergency response time and made local planning more proactive,” he said.
Beyond code and models, Akajiaku revealed that he is building human capacity, and within two years, has trained more than 200 students in AI and machine learning, with a focus on climate-related applications.
He added that he also mentors young researchers and runs hands-on workshops using tools like Google Earth Engine, Machine learning and Python for spatial analytics.
“I have been a guest speaker at regional conferences, including the AI for Climate Summit, where I challenged stakeholders to prioritise tech-enabled climate adaptation. My goal is to democratise climate tech. We are not just solving for today — we are equipping people to solve for tomorrow,” he said.
Now based in the UK, Akajiaku further revealed that he continues to work on cross-continental environmental tech collaborations and explores how data science can address urban flooding, carbon tracking, and renewable energy forecasting in emerging economies.
He noted that he is also working toward publishing his upcoming project on machine learning for mangrove monitoring — a critical step for preserving biodiversity in coastal zones.
“Climate change is personal for me. It is happening in my hometown, to my people. That is why I build,” he said.
He further noted that apart from his core focus areas ─ flood prediction, shoreline monitoring, environmental impact analysis, and climate education ─ he is also into tech stack technologies such as Python, Google Earth Engine, QGIS, Tableau, GitHub, and AWS.
