Pradhan, B

About

Distinguished Professor Biswajeet Pradhan is an internationally established scientist in the field of Spatial Intelligence, GeoAI, Geospatial Information Systems (GIS), Remote Sensing and Image Processing, Complex Modeling/Geo-Computing, Machine Learning, and Soft-Computing Applications, Natural Hazards and Environmental Modeling, and Remote Sensing of Earth Observation. He is also a distinguished professor at the University of Technology, Sydney. He is listed as the World’s Most Highly Cited Researcher by Clarivate Analytics Report for five consecutive years: from 2006 to 2020 as one of the world’s most influential minds. In 2018-2020, he was awarded a World Class Professor by the Ministry of Research, Technology and Higher Education, Indonesia. He is a recipient of the Alexander von Humboldt Research Fellowship from Germany. In 2011, he received his habilitation in “Remote Sensing” from Dresden University of Technology, Germany. Between February 2015 and February 2022, he served as “Ambassador Scientist” for the Alexander Humboldt Foundation, in Germany. Professor Pradhan has received 55 awards since 2006 in recognition of his excellence in teaching, service, and research. Out of his more than 988 articles (Total Citation: 95,644, H-index: 158, i10-index: 789), more than 950 have been published in Science Citation Index (SCI/SCIE) technical journals. He has written 15 books and 62 book chapters.

Employment

• University of Technology Sydney Distinguished Professor
  2018 - Present
• Universiti Putra Malaysia Faculty
  2007 - Present
• University Of Technology Sydney

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (1661)

• A Synergistic Approach Using Machine Learning and Deep Learning for Forest Fire Susceptibility in Himalayan Forests
  Journal of the Indian Society of Remote Sensing 2026 DOI: 10.1007/s12524-025-02157-4
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• A holistic approach to seismic risk by integrating preparedness into hybrid deep learning and ANP-based spatial modeling: A case study of Coimbatore city, India
  Sustainable Cities and Society 2026 DOI: 10.1016/j.scs.2026.107699
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• Integrating climate change, land-use dynamics, and explainable AI for future landslide susceptibility assessment in the Western Ghats
  Physics and Chemistry of the Earth, Parts A/B/C 2026 DOI: 10.1016/j.pce.2026.104764
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• A Comparative Study of Spatial MCDM Techniques for Flash Flood Risk Mapping in Northeastern Bangladesh
  Earth Systems and Environment 2026 DOI: 10.1007/s41748-025-00942-2
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• A novel robust model: Dimensionless ensemble machine learning to predict behavior of geopolymer concrete
  Structures 2026 DOI: 10.1016/j.istruc.2026.112552
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• Hotspots and drivers of flood damage in Assam, India: A spatio-temporal assessment of flood-induced damage and their driving factors
  International Journal of Disaster Risk Reduction 2026 DOI: 10.1016/j.ijdrr.2026.106266
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• Analysing Urban Environmental Dynamics through Multi-Sensor Remote Sensing and Deep Learning: An Integrated Classification-Regression Framework
  Korean Journal of Remote Sensing 2026 DOI: 10.7780/kjrs.2026.42.4.5
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• Climate-Adaptive Urban Planning: Quantitative Assessment of Drought Impact and Practical Strategies for Climate-Resilient Urban Green Spaces
  Remote Sensing 2026 DOI: 10.3390/rs18152531
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• Coupled hydrological–hydrodynamic modelling of GLOFs triggered by extreme precipitation in the Western Himalayas
  Journal of Hydrology: Regional Studies 2026 DOI: 10.1016/j.ejrh.2026.103738
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• Comparative evaluation of ML and DL approaches for spatial landslide modeling in Tehri Garhwal, India
  Spatial Information Research 2026 DOI: 10.1007/s41324-026-00701-z
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