The State University Of Zanzibar

BIO

Masoud Hamad is an Assistant Lecturer at the State University of Zanzibar (SUZA) in the School of Computing, Communication, and Media Studies, specializing in Geospatial Software Engineering, Big Data, and Artificial Intelligence. He is also a Django Software Foundation Member and an active advocate for open-source software development.

With a Master’s in Computer Applications from Vellore Institute of Technology (VIT), India, his research focused on Climate Big Data and Deep Learning. He is deeply engaged in GeoAI, Big Earth Data, Remote Sensing, Google Earth Engine, and Geospatial Data Science, applying these technologies to study environmental changes using cloud computing and open-source spatial data infrastructures.

Masoud has been a key contributor to several international projects, including Resilience Academy, GeoICT4e, and the Climate Risk Database, collaborating with organizations such as the World Bank, the Finnish Government, USAID, the Government of Tanzania, and the Revolutionary Government of Zanzibar. As a Software Developer and Team Leader, he has led projects focusing on geospatial data analysis, environmental monitoring, and sustainable development.

He has nearly nine years of experience in teaching, mentoring, and research, playing a pivotal role in training students in Software Engineering, Artificial Intelligence, and Geospatial Technologies. As a mentor for the SUZA App Club and FOSS Club, he guides students aspiring to excel in open-source software, GIS, and AI-driven geospatial solutions.

Additionally, Masoud is the co-founder of HMY Company, specializing in Consultation, Development, and Training in emerging technologies. He has also developed several open-source packages designed for advanced geospatial analysis and visualization, contributing to the global open-data and spatial data infrastructure movements.

research interests

  • GeoAI & Big Earth Data
  • Remote Sensing & Google Earth Engine
  • Geospatial Data Science & GIS
  • Open Source & Spatial Data Infrastructure
  • Cloud Computing for Environmental Analysis
  • Artificial Intelligence & Deep Learning for Geospatial Applications