Tool
AI for Oceans
AI tools to support physical and digital technologies, enhancing monitoring, understanding, modeling, and forecasting of climate change, extreme events, and marine species management.
Scientific Paper · source-verified
A Sentinel-3 Foundation Model for Ocean Colour
Dawson et al. · 2025-09-25
This study introduces a new foundation model based on the Prithvi-EO Vision Transformer, pre-trained on Sentinel-3 ocean color data, to enhance marine Earth observation. By fine-tuning on chlorophyll and primary production tasks, the model outperforms traditional baselines, showing that self-trained AI can extract detailed spatial patterns from limited labeled data and improve monitoring of ocean ecosystems and climate processes.
Source identity and required public metadata were editorially reviewed on 2026-09-15.
Show citation
Dawson et al.. 2025-09-25. A Sentinel-3 Foundation Model for Ocean Colour. https://arxiv.org/pdf/2509.21273 (AI & Environment Resource Hub; record paper-393; collection snapshot 2026-09-15).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
topic
orientation
Available source metadata
- Author or creator
- Dawson et al.
- Publisher
- arXiv
- Publication date
- 2025-09-25
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source status
- verified
- Last verified
- 2026-09-15T00:00:00.000Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Water & Oceans
- Legacy subdomain
- Sentinel-3 Foundation Models for Ocean Conservation and Land Mapping
- journal
- arXiv
Provenance and review
Selected fields checked: · 2026-09-06. Editorial review confirmed this record against its current source page/file; limited or generic machine-readable metadata prevented automatic corroboration. Verification source ↗
Source sheet: Scientific Paper · Row 397 · Original ID: paper-393.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.
Suggest a correction for this record →Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Climate and greenhouse gases; Water; Biodiversity and ecosystems; Oceans and coasts
Shared topics: Climate and greenhouse gases; Water; Biodiversity and ecosystems; Oceans and coasts
Scientific Paper · 2025-04-10
AI-Based Channel Infused Coral Identification and Tracking Algorithm for Autonomous Underwater Vehicles
This study develops a deep learning–based coral identification and tracking algorithm for autonomous underwater vehicles, enabling real-time monitoring of reef health to support climate-informed conservation efforts.
Shared topics: Research tools and geospatial methods; Water; Biodiversity and ecosystems; Oceans and coasts
Tool
CyFi: Cyanobacteria Finder
CyFi is a command line tool that uses satellite imagery and machine learning to estimate cyanobacteria levels in small, inland water bodies. Cyanobacteria is a type of harmful algal bloom (HAB), which can produce toxins that are poisonous to humans and their pets, and can threaten marine ecosystems.
Shared topics: Research tools and geospatial methods; Climate and greenhouse gases; Water; Oceans and coasts
Scientific Paper · 2024-02-01
Hydroclimatic Projection: Statistical Learning And Downscaling Model For Rainfall And Runoff Forecasting
Climate change projections for the study area predict reduced rainfall and surface runoff, indicating a future water-stressed scenario, especially under SSP (Shared Socioeconomic Pathways) 4.5 and SSP 8.5 scenarios.