Dr. Giuseppe Amatulli
GIS Modeller
Interests
Hydrological modelling Geomorphology analysis Machine learning Large-scale geocomputation
Education
- PhD in Forestry, 2005
Basilicata University - Italy - MSc Geo-information sciences, 2004
Wageningen University - The Netherlands - BSc & MSc Forestry, 2000
Bari University - Italy
Dr. Giuseppe Amatulli is a research scientist at Yale University and focuses on large-scale & open-source geocomputation. He has been at the forefront of the Geomorpho90m and Hydrography90m datasets and is actively engaged in teaching the latest advances in geocomputation and machine learning.
Career
- since 2016 - Yale School of the Environment, Yale University (USA)
- 2013-2016 - Department of Ecology and Evolutionary Biology, Yale University (USA)
- 2012-2013 - South Dakota State University (USA)
- 2006-2011 - Institute for Environment and Sustainability (action Forest) of the Joint Research Centre
- 2005-2006 - Departamento de Geografia y ordenacion del territorio, Zaragoza University (Spain)
Projects
Publications (12)
Environment90m – globally standardized environmental variables for freshwater science at high spatial resolution (2026) · Earth System Science Data 2026, 1541–1559
GeoFRESH – an online platform for freshwater geospatial data processing (2024) · International Journal of Digital Earth, 2391033
Time series of freshwater macroinvertebrate abundances and site characteristics of European streams and rivers (2024) · Scientific Data, 11, 601
hydrographr: an R package for scalable hydrographic data processing (2023) · Methods in Ecology and Evolution, 14, 2953-2963
DOI Project R-package Installation SDM workflow Connectivity workflow Authors & details
The Global EPTO Database: worldwide occurrences of aquatic insects (2023) · Global Ecology and Biogeography
Long-term trends in crayfish invasions across European rivers (2023) · Science of the Total Environment, 867, 161537
Hydrography90m: A new high-resolution global hydrographic dataset (2022) · Earth System Science Data, 14, 4525–4550
Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers (2020) · Scientific Data, 7, 162
Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework (2020) · Scientific Data, 7, 161
High-resolution stream network delineation using digital elevation models: assessing the spatial accuracy (2018) · PeerJ Preprints, 6, e27109v1
A suite of global, cross-scale topographic variables for environmental and biodiversity modeling (2018) · Scientific Data, 5, 180040