MAPPING COASTAL TYPOLOGY USING PUBLICLY AVAILABLE EARTH OBSERVATION DATA AND DEEP NEURAL NETWORKS

Floris Calkoen; Arjen Luijendijk; Antonio Moreno Rodenas; Stefan Aarninkhof

doi:10.9753/icce.v37.management.158

Núm. 37 (2022), Coastal Management, Environment, and Risk

Núm. 37 (2022)

MAPPING COASTAL TYPOLOGY USING PUBLICLY AVAILABLE EARTH OBSERVATION DATA AND DEEP NEURAL NETWORKS

Coastal Management, Environment, and Risk

Publicado 2023-09-01

Floris Calkoen
Arjen Luijendijk
Antonio Moreno Rodenas
Stefan Aarninkhof

Floris Calkoen

Arjen Luijendijk

Antonio Moreno Rodenas

Stefan Aarninkhof

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MAPPING COASTAL TYPOLOGY USING PUBLICLY AVAILABLE EARTH OBSERVATION DATA AND DEEP NEURAL NETWORKS. (2023). Coastal Engineering Proceedings, 37, management.158. https://doi.org/10.9753/icce.v37.management.158

Resumen

Given the urgent need for a high-resolution geomorphological characterization of the coast, we developed an innovative method to classify large amounts of thumbnail images, extracted from publicly available Earth Observation data, into an erosion-dedicated coastal typology. The dataset is an valuable asset for coastal managers who aim for sustainable use of the coastal zone.

PDF (Inglés)

Referencias

Ranasinghe (2020): On the need for a new generation of coastal change models for the 21st century. Scientific Reports, 10 (1)

Salman et al. (2004): Living with coastal erosion in Europe: Sediment and Space for Sustainability. Eurosion project reports.

Vousdoukas et al. (2020): Economic motivation for raising coastal flood defenses in Europe. Nat Commun, 11 (1)

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.

Derechos de autor 2023 Floris Calkoen, Arjen Luijendijk, Antonio Moreno Rodenas, Stefan Aarninkhof

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MAPPING COASTAL TYPOLOGY USING PUBLICLY AVAILABLE EARTH OBSERVATION DATA AND DEEP NEURAL NETWORKS

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