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A 2D representation of the full point cloud from the 50 m LiDAR dataset accurately positioned in 3D (x, y, z). The outline indicates the subsection shown in B. (B) The cleaned 50 m point cloud used to create a 3D representation of the eelgrass meadow. Points are scaled by their intensity values. (C) The intensity scale of the 50 m, annotated vegetation points before classification (colour-scaled points). Plots (D–F) display the same information, respectively, of plots (A–C), for the 25 m LiDAR dataset. This figure was created using CloudCompare (2024).

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3D eelgrass mapping with drone LiDAR

3D eelgrass mapping with drone LiDAR

Published open access in Remote Sensing, the article demonstrates how drone-borne topobathymetric LiDAR can map eelgrass (Zostera marina) habitat in ...
SeaBee's Drone and Sensor Infrastructure Report

SeaBee’s Drone and Sensor Infrastructure Report

The report is an extensive overview of the three primary drone categories employed by SeaBee: aerial, surface, and underwater drones, ...
Overview of the SeaBee Data Platform Report

Overview of the SeaBee Data Platform Report

The report discusses how the SeaBee data platform provides researchers with powerful automated processing workflows capable of rapidly handling data ...
SeaBee Impact Report

SeaBee Impact Report

The report highlights SeaBee's role in establishing a national center for drone-based services, significantly enhancing the precision, efficiency, and scope ...
Drone data collection protocol using DJI Mavic 3E/3M RTK - Seabird mapping with SeaBee

Drone data collection protocol using DJI Mavic 3E/3M RTK – Seabird mapping with SeaBee

The document "Drone data collection protocol using DJI Mavic 3E/3M RTK – Seabird mapping with SeaBee" outlines a standardized method ...
Drone and ground-truth data collection, image annotation and machine learning: A protocol for coastal habitat mapping and classification

Drone and ground-truth data collection, image annotation and machine learning: A protocol for coastal habitat mapping and classification

Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution ...
Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques

Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques

Monitoring macroplastics in aquatic and terrestrial ecosystems: Expert survey reveals visual and drone-based census as most effective techniques, Science of ...
Automated monitoring of the early life stages of fish (Phd Thesis)

Automated monitoring of the early life stages of fish (Phd Thesis)

This thesis presents an automated imaging system developed to improve the monitoring of early life stages in fish, which are ...
Method development for mapping kelp using drones and satellite images: Results from the KELPMAP-Vega project

Method development for mapping kelp using drones and satellite images: Results from the KELPMAP-Vega project

Method development for mapping kelp using drones and satellite images: Results from the KELPMAP-Vega project ...
An investigation into multimodal UAV imaging for ocean color and benthic mapping (MSc thesis)

An investigation into multimodal UAV imaging for ocean color and benthic mapping (MSc thesis)

Saatvedt OKO. 2024. An investigation into multimodal UAV imaging for ocean color and benthic mapping (MSc thesis). Department of Engineering ...

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