2025-11-30 –, Audition Room - 2nd Floor Language: English
Earth Observation (EO) combined with OpenStreetMap (OSM) has become a powerful driver of geospatial intelligence across Africa, supporting everything from disaster response to agricultural planning and infrastructure development. But despite its potential, many EO-OSM workflows remain manual, fragmented, and difficult to scale, especially in the fast-paced environments where they’re most needed.
This work introduces a new frontier: Agentic AI—autonomous, goal-driven systems that can reason, plan, and interact with tools, APIs, and data to carry out complex tasks with minimal human input. The python-based geospatial and machine learning tools, can serve as intelligent co-pilots for EO-integrated mapping workflows that can: rapidly respond to disasters by fusing Sentinel radar data with OSM building footprints to identify flood zones and assess impacted communities; spot infrastructure gaps by analyzing Sentinel-2 imagery alongside OSM road networks to recommend missing roads or generate MapRoulette tasks; and automate land use classification with open EO datasets like Landsat and Sentinel-2, where agents handle everything from data preprocessing to training models and generating visual maps.
This work also covers practical knowledge, reusable Python workflows, and a fresh perspective on how agentic AI can power faster, smarter, and more scalable mapping across Africa and beyond.
Isah Abdul-Azeez is a Geospatial Machine Learning Engineer with a strong background in remote sensing, AI-driven analytics, and open-source geospatial technologies. His work focuses on designing intelligent, scalable solutions for agriculture, disaster risk management, and infrastructure mapping across underserved and data-scarce regions in Africa. With a multidisciplinary foundation in chemistry and environmental sciences, Isah blends scientific rigor with advanced geospatial and machine learning techniques to tackle real-world problems.
Over the past four years, he has led and contributed to projects that utilize Earth Observation data—ranging from optical to radar imagery—alongside platforms like OpenStreetMap to deliver actionable insights to local governments, NGOs, and rural communities. His tools have supported precision agriculture, flood monitoring, and road network expansion, impacting farmers across Nigeria and mapping thousands of square kilometers of terrain.
Isah is also an advocate for open data and reproducible science that democratize access to geospatial intelligence. He actively participates in humanitarian mapping, contributes to open-source projects, and mentors emerging data scientists and mappers.
Driven by a vision to empower African communities with data and intelligent automation, he continues to explore how technologies like Agentic AI can revolutionize the geospatial ecosystem and amplify local impact across the continent.