Work

Improving nearshore bathymetry mapping through spatially adaptive machine learning [poster]

Year: 2025

Type: article

Abstract: Bathymetry plays a pivotal role in ocean science and blue economy applications What is the current state of seafloor mapping? The land-sea interface remains significantly under-surveyed. In situ observations: Adverse sea conditions; Low efficiency. Remote sensing: LiDAR (expensive); Satellite (low resolution) Unmanned Aerial Vehicle (UAV): Efficient and flexible; High spatial resolution; Optically derived bathymetry; Statistical/empirical approach; Machine learning (ML) techniques: GRF and RF.

Source: Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)

Author: Dias, Telmo

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

SDG: Life below water

Open Access status: green

Funder: Fundação para a Ciência e a Tecnologia

Awards: Centro de Investigação em Gestão de Informação, Centro de Investigação em Gestão de Informação, G6997658380, G7506635455

OpenAlex ID: W7198693239

FWCI:

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