Addressing the most pressing African challenges with GeoAI

By transforming remote sensing data into actionable insights for sustainable development, geospatial AI (GeoAI) is rapidly emerging as a vital tool to address the mounting challenges and urgent needs in Africa. These include climate change, rapid urbanization and food insecurity, which affected 307 million people across the continent (over 20% of the population) in 2024, according to a UN report. The situation is compounded by limited resources and data gaps. The grassroots movement GeoAI-Africa aims to empower governments, communities and professionals with GeoAI tools that combine satellite and sensor data with machine learning. This will enable them to monitor environmental change, forecast food production, plan cities and respond to floods or droughts in near real time.

The challenges in Africa vary significantly from one region to another. But across the continent, the problems are immediate and severe. Between 2020 and 2022, over 30 million people faced drought-driven food insecurity across the Horn of Africa. In 2022, figures showed that roughly 230 million urban dwellers in sub-Saharan Africa lived in slums. By the end of 2023, internally displaced populations due to conflict and climate disasters had reached some 35 million, straining disaster response systems that lack timely, accurate maps. In 2024, more than one billion Africans (nearly two-thirds of the population) could not afford a healthy diet. In the same year, record floods in over 27 countries displaced about four million people and killed approximately 2,500.

A people-first movement solving concrete problems

These challenges underscore the urgent need for innovative, locally grounded solutions that can scale across diverse African contexts. In response, GeoAI-Africa has emerged as a grassroots movement aiming to empower communities and professionals with geospatial AI tools tailored to real-world problems. While still in its early stages, the network’s vision is to make advanced geospatial tools accessible, contextualized and impactful.

Climate impacts are already shortening planting windows, shifting pest ranges and increasing the frequency of extreme rainfall. Members of GeoAI-Africa support local adaptation by converting satellite and sensor data into high-frequency, localized climate indicators and hazard maps that show where water stress, soil degradation or flood risk are escalating. These products can help decision-makers, NGOs and communities target interventions such as emergency seed distribution, drought-tolerant crop support and community drainage upgrades.

Alignment with SDGs

Indeed, food insecurity in many regions is driven by subtle, distributed signals: failing crops, soil moisture decline, or an emerging pest front. These issues directly relate to the United Nations Sustainable Development Goals (SDGs), specifically SDG 2: Zero Hunger and SDG 13: Climate Action. GeoAI-Africa trains members to build pipelines that detect stressed vegetation, classify crop types and estimate yields using imagery time series and machine learning, enabling early action such as adjusted planting calendars or targeted extension services. Such early action can prevent localized shocks from becoming regional famines.

Urban problems, tied to SDG 11: Sustainable Cities and Communities, are different but equally urgent. When informal settlements grow overnight, traditional surveys and paper maps cannot keep up. GeoAI-Africa’s mapping workflows teach young professionals and GeoAI enthusiasts how to quickly and accurately extract valuable insights such as building footprints and road networks from aerial and satellite imagery. This allows city planners to identify where water, sanitation or health services are most needed and where evacuation routes are blocked before the next disaster. Translating imagery into operational maps shortens the time between insight and service delivery from months to days or even hours.

Disaster response, closely aligned with SDG 3: Good Health and Well-being, is perhaps the place where speed matters most, particularly in the context of reducing preventable deaths. Members of the community work on rapid, automated workflows that within a matter of hours convert raw satellite feeds into flood inundation maps, damage assessments and population exposure layers. This accelerates logistical planning, enables quicker evacuations, and makes sure relief convoys reach the most affected communities first.

Bringing leading organizations and local talent together

What allows GeoAI-Africa to tackle these problems is the deliberate coupling of grassroots energy with technical expertise from leading research labs. Members affiliated with organizations including IBM Research, Microsoft AI for Good Lab, Google Research, Data Science Nigeria and more regularly mentor community teams, share operational practices and introduce scalable tools. These experts contribute practical guidance on building robust models, operationalizing Earth-observation pipelines and productionizing models so they run reliably under real-world data constraints.

This collaboration is reciprocal. Researchers and mentors from the community bring institutional methods into workshops and hackathons, while students and local professionals contribute on-the-ground contextual knowledge that fundamentally increases impact. Community projects therefore reflect both global best practices and local specificity.

Training as an instrument of impact and sustainability

GeoAI-Africa is committed to advancing GeoAI education and capacity building across the continent through workshops, master classes, hands-on training initiatives and hackathons at events like AMLD Africa, the Deep Learning Indaba and university-led programmes. In GeoAI-Africa workshops, participants don’t just learn theory; they gain hands-on exposure to geospatial datasets, machine learning models and open-source tools. Using interactive tools, they preprocess satellite imagery, train AI models to identify features like rooftops or croplands, and then validate their predictions against ground-truth data.

Solving real problems

GeoAI is already showing its promise globally, and Africa stands to benefit greatly. For GeoAI-Africa, the focus today is on building capacity, fostering partnerships and experimenting with prototypes that address urgent issues. The long-term vision is to scale these efforts into systems that support governments, NGOs and communities in addressing food insecurity, urban growth and climate disasters. The model is simple but powerful: start with real problems, learn together and grow capacity through open collaboration. In doing so, GeoAI-Africa is helping ensure that geospatial AI becomes not just a research frontier, but a practical tool for protecting lives and livelihoods across the continent.

Further reading

https://africacenter.org/spotlight/record-levels-of-flooding-in-africa-compounds-stress-on-fragile-countries/
https://www.undrr.org/resource/horn-africa-floods-and-drought-2020-2023-forensic-analysis
https://apnews.com/article/global-report-africa-food-insecurity-united-nations-0d366a4042d4524a79cb5b47a94bc0cd
Urban slums in sub-Saharan Africa in 2022: https://unstats.un.org/sdgs/report/2023/goal-11/
https://earth.org/floods-and-droughts-driving-increasing-displacement-in-africa-report/

www.geoai-africa.com
Notebooks and starter code: https://github.com/GeoAIAfrica
Blog in ‘GeoAI for a Sustainable Future in Africa’ workshop (Dakar 2024): https://deeplearningindaba.com/blog/2024/10/geoai-for-a-sustainable-future-in-africa/
https://geoaiafrica.github.io/ (GeoAI-Africa workshop/call for abstracts)
https://appliedmldays.org/events/amld-africa-2024/workshops/geoai-in-africa-harnessing-satellite-data-for-climate-resilience
IBM & African Risk Capacity research (AI for climate risk and floods): https://research.ibm.com/blog/ibm-arc-africa-climate

Microsoft Disaster Response (MSDR) programme: https://www.microsoft.com/en-us/corporate-responsibility/philanthropies/disaster-response