SeaBee is creating a comprehensive national research infrastructure, enabling sharing and use of data, collected with drones, to better understand Norway’s natural environment. SeaBee trains machine learning algorithms to analyse drone data and facilitate automated data analysis. This allows researchers to do their work faster, better, and more efficiently.
SeaBee and SeaBirds – collecting drone images in the field
Two field teams from NINA were out in May 2023. Led by Sindre Molværsmyr (NINA), the teams mapped sea gull and cormorant breeding colonies along the southern Norwegian coast from the Swedish border to Karmøy (more than ~500 km of coastline). The goal was to count seabirds through collecting drone images of seabird populations. The data collected from the drone images can then be compared with historical datasets, and data collected using traditional methods.
Traditional methods of data collection are people going to the seabird colony and counting the seabirds, or taking thousands of photos with a handheld camera from the window of a small airplane. The seabird population counting work also contributes to SEAPOP (Om SEAPOP – SEAPOP).
In total, 440 drone missions were flown by two teams during two weeks from 15 – 29th May, 2023, each using a DJI MAVIC 3 equipped with a RGB camera (SeaBee tech). The surveys mostly covered open-nesting species, like gulls and cormorants.
With very nice weather for most of the field mission, the teams collected around 100 thousand drone images of seabird populations during the 440 missions. The largest missions collected around 2000 images to cover larger islands along the coast. Both teams covered about 150 km of coastline each day, one team with their own boat and the other with good help from Skjærgårdstjenesten or other local boat drivers.
The teams covered most known bird populations in the mission area. All missions were logged and added to the map and data visualisations. This will help for next year’s field mission planning, as colonies that were missed this year are easily seen and located in the map visualisations.
Our field work and data collection could not have been done at this scale without SeaBee – sending data for processing by pressing enter on my computer in the evenings was very efficient. The amount of data collected would be impossible to analyse manually, it would take many months to analyse drone images from 400 colonies by hand – Sindre Molværsmyr, NINA
Large scale data upload
NINA’s field campaign is one of the first examples of the SeaBee infrastructure operating on large-scale data handling, with drone images uploading directly to the SeaBee data server directly from the field at the end of each day. This is a great achievement for SeaBee, which not only saves time in the field, but allows more efficient mapping and monitoring of large geographic areas with better data quality.
James Sample (NIVA) and the SeaBee Data Platform team have worked hard to get to this point (SeaBee Data Platform components and how it works). Using code deployed on Sigma2, an automated script runs every hour to identify, process and publish new datasets uploaded by scientists in the field.
It was an exciting first test of SeaBee as “data infrastructure”. I really enjoyed being able to explore the new mission data published each morning to see what I could find – James Sample, NIVA
SeaBee Data Pipeline
The SeaBee data infrastructure was valuable during this mission, as having data processed in near real-time allowed the field teams to review data and adjust settings and drone flying strategies during the mission, to ensure the best quality drone images were collected.
In total, around 1 TB of raw images from drone flights were collected and uploaded to the SeaBee data repository, which led to around 15 TB of data once it had been processed (Digital Terrain models (DTMs), orthophotos, point clouds, etc).
The SeaBee data infrastructure ran well for processing such a large amount of data. On average, missions took around 90 minutes to process. Missions with approximately 2000 images took around 1 day to process. After processing, the final products were automatically published to SeaBees GeoNode server. Currently, there are more than 500 data resources published on the server.
This project has been an important achievement for SeaBee as it demonstrates how useful a drone-based infrastructure is for seabird research and mapping, and it serves as a good example of how efficient SeaBee can be for large-scale mapping projects and handling of big data. – Kasper Hancke, NIVA
The next steps for this seabird mapping project include analysing the drone images that have been collected to estimate seabird population numbers. This will involve training a new AI model, and annotating drone images work as bird species were encountered that are not in the current AI models (for more information, check using Artifical Intelligence and drone data). Also, some comparisons will be made between the data collected using drones and that collected using traditional methods.
In terms of what will happen next for the SeaBee data infrastructure, this field mission provided some very useful lessons for processing large scale datasets. Better error handling systems will be developed, and the machine learning from NR will be integrated to further automate the infrastructure.