M
Michele Melchiorri
Publications - 19
Citations - 790
Michele Melchiorri is an academic researcher. The author has contributed to research in topics: Population & Urbanization. The author has an hindex of 10, co-authored 19 publications receiving 367 citations.
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Journal ArticleDOI
Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer
Michele Melchiorri,Aneta J. Florczyk,Sergio Freire,Marcello Schiavina,Martino Pesaresi,Thomas Kemper +5 more
TL;DR: Light is shed on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting.
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Automated global delineation of human settlements from 40 years of Landsat satellite data archives
Christina Corbane,Martino Pesaresi,Thomas Kemper,Panagiotis Politis,Aneta J. Florczyk,Vasileios Syrris,Michele Melchiorri,Filip Sabo,Pierre Soille +8 more
TL;DR: In this article, the authors present an analysis of Earth observation data records collected between 1975 and 2014 for assessing the extent and temporal evolution of the built-up surface in the frame of th...
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Principles and Applications of the Global Human Settlement Layer as Baseline for the Land Use Efficiency Indicator—SDG 11.3.1
TL;DR: A global estimate for the Land Use Efficiency (LUE) indicator—SDG 11.3.1, for circa 10,000 urban centers, calculating the ratio of land consumption rate to population growth rate between 1990 and 2015 is presented.
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Applying the Degree of Urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation
Lewis Dijkstra,Aneta J. Florczyk,Sergio Freire,Thomas Kemper,Michele Melchiorri,Martino Pesaresi,Marcello Schiavina +6 more
TL;DR: The degree of urbanisation is a new definition of cities, towns and semi-dense areas, and rural areas endorsed by the United Nations Statistical Commission as discussed by the authors, which is used to classify cities and towns as rural areas in Africa and Asia and as urban areas in other parts of the world.
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Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery
Christina Corbane,Vasileios Syrris,Filip Sabo,Panagiotis Politis,Michele Melchiorri,Martino Pesaresi,Pierre Soille,Thomas Kemper +7 more
TL;DR: In this paper, a deep-learning-based framework was proposed for the extraction of built-up areas at a spatial resolution of 10m from a global composite of Sentinel-2 imagery.