Synergising spatio-temporal big data and local knowledge for climate-adaptive green infrastructure planning in urban africa: pathways and pitfalls

African cities, at the nexus of rapid urbanisation and escalating climate change, urgently require innovative approaches for climate-adaptive Green Infrastructure (GI) planning. While Spatio-temporal Big Data (STBD) offers powerful analytical capabilities, its integration with the rich, context-specific insights of Local and Indigenous Knowledge (LK/IK) remains a critical, underexplored frontier. This study critically examines the imperatives, pathways, and pitfalls of synergising STBD and LK/IK for enhanced GI planning in urban Africa. It argues that transcending the limitations of relying solely on either technology-driven data or localised knowledge requires fostering a dynamic interplay between them. This study maps out tangible methodological, technological, institutional, and ethical pathways for effective knowledge co-production. Simultaneously, it critically assesses significant pitfalls, including deep-seated epistemological divides, pervasive power asymmetries, resource constraints, and the dangers of tokenism that can undermine genuine integration. Ultimately, this study contends that fostering this synergy is not merely beneficial but essential for developing technically sound, socially equitable, culturally appropriate, and ultimately, more sustainable GI solutions in African urban contexts, thereby contributing to resilient and just urban futures.