2026-06-28 –, Kilimanjaro - 2nd flooor Language: English
Earth Observation (EO) and OpenStreetMap (OSM) are increasingly important for geospatial intelligence across Africa, supporting work in disaster response, agricultural planning, infrastructure mapping, and environmental monitoring. But in practice, many EO and mapping workflows remain manual, fragmented, and difficult to scale, especially in fast-moving or data-scarce contexts.
This work explores how agentic AI can support EO workflows as a geospatial co-pilot. The focus is on AI agents that can help plan and coordinate repetitive parts of the workflow: finding relevant data, running preprocessing steps, calling Python geospatial tools, organizing analysis tasks, and packaging outputs in a way that people can inspect and validate.
It will cover a high-level view of the current workflow, focusing on how an agent can coordinate open Earth Observation datasets such as Sentinel and Landsat with Python-based geospatial tools. This work will discuss what the system can currently support, where human review is still necessary, and how this approach could later connect more deeply with OSM-based validation and mapping workflows.
The session will share the motivation behind the work, the overall system idea, lessons from building EO-integrated agent workflows, and a realistic view of the opportunities and limits of using agentic AI to support geospatial analysis in African contexts.
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.