In this episode, I caught up with Ibrahim Salihu Yusuf from InstaDeep’s AI for Social Good team to hear the story behind InstaGeo, an open-source geospatial machine learning framework built to make multispectral satellite data easy to use for real-world applications. Ibrahim explains how the 2019–2020 locust outbreak exposed a gap between freely available satellite imagery, existing machine learning models, and the lack of tools to turn raw data into model-ready inputs. He walks through how InstaGeo bridges this gap - fetching, processing, and preparing multispectral data; fine-tuning models such as NASA IBM’s Prithvi; and delivering end-to-end inference and visualisation in a unified app. The conversation also covers practical use cases, from locust breeding ground detection to damage assessment, air quality, and biomass estimation, as well as the team’s efforts to partner with field organisations to drive on-the-ground impact.

Bio: Ibrahim is a Senior Research Engineer and Technical Lead of the AI for Social Good team at InstaDeep’s Kigali office, where he applies artificial intelligence to address real-world challenges and drive social impact across Africa and beyond. With expertise spanning geospatial machine learning, computer vision, and computational biology, he has led high-impact projects in food security, disaster response, and immunology research. He also leads the development of InstaGeo, a platform designed to democratise access to AI-powered insights from open-source satellite imagery, reflecting his commitment to using cutting-edge AI for meaningful societal benefit.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.satellite-image-deep-learning.com

Democratising access to GeoAI with InstaGeo