Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/105318
Title: Unsupervised learning of satellite images enhances discovery of late Miocene fossil sites in the Urema Rift, Gorongosa, Mozambique
Authors: Coelho, João Pedro de Oliveira 
Anemone, Robert L.
Carvalho, Susana 
Keywords: Geospatial Paleontology; Southeast Africa; Late Miocene; Remote Sensing; Unsupervised Learning
Issue Date: 2021
Publisher: PeerJ
Project: SFRH/BD/122306/2016 
The Boise Trust Fund 
Gorongosa Restoration Project, the National Geographic Society, the John Fell Fund Oxford, and the Leverhulme Trust 
Serial title, monograph or event: PeerJ
Volume: 9
Abstract: Paleoanthropological research focus still devotes most resources to areas generally known to be fossil rich instead of a strategy that first maps and identifies possible fossil sites in a given region. This leads to the paradoxical task of planning paleontological campaigns without knowing the true extent and likely potential of each fossil site and, hence, how to optimize the investment of time and resources. Yet to answer key questions in hominin evolution, paleoanthropologists must engage in fieldwork that targets substantial temporal and geographical gaps in the fossil record. How can the risk of potentially unsuccessful surveys be minimized, while maximizing the potential for successful surveys?
URI: https://hdl.handle.net/10316/105318
ISSN: 2167-8359
DOI: 10.7717/peerj.11573
Rights: openAccess
Appears in Collections:I&D CFE - Artigos em Revistas Internacionais

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