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Remote Sensing Bio‑Digester DatasetOverviewTo the best of our knowledge, this is the first large-scale satellite dataset of bio‑digesters, with facility‑level and part‑level annotations. It comprises high‑resolution aerial and satellite imagery of bio‑digester sites across France’s Grand Est region, drawn from multiple sources. Ground‑truth labels are geolocated segmentation masks for three classes:  Whole installation (entire bio‑digester site)  Anaerobic digestion tanks (internal tanks)  Biomass piles (feedstock storage areas)Code available On GithubCompositionTraining & validation sets include SPOT, Sentinel, and aerial modalities. Training is made easy using MMRotate as annotations are also available in DOTA format.Resolutions: 0.5 m, 1.5 m, and 5 m per pixel.  Modalities: Aerial, SPOT, and Sentinel imagery—to study transferability and resolution impact.  Labels remain geolocated; due to modality mismatches, some labels transfer poorly, so aerial annotations serve as the definitive reference. Coordinates provided in EPSG:2154.Directory Structure├── README.md├── res_0.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   └── vpv│       ├── train│       └── val├── res_1.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   ├── test│   │   ├── image│   │   ├── label│   │   └── meta│   └── vpv│       ├── train│       └── val└── res_5    ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val    ├── label    │   ├── train    │   └── val    └── vpv        ├── train        └── valTestingTest SplitCovers the entire French department of Marne, pre‑tiled at 1.5 m resolution in 1000×1000 px tiles (~5000 tiles).Environment & ModalitiesAvailable only in aerial and SPOT modalities at 1.5 m resolution.A precomputed tiling facilitates large‑scale evaluation without engineering overhead.Objective & Metrics- Detect all bio‑digester sites within 200 m accuracy (AP@200 m as introduced in the paper).  - Augmented with newly discovered Marne‑region digesters to better estimate precision at scale:    - SPOT: 29 sites    - Aerial: 27 sites (including 4 under construction)  VisualizationOptionally, use vpv to explore the dataset:vpv ac aw nw ./res_1.5/image/BDORTHO/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/PLEIADES/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SPOT/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SENTINEL/train/ svg:./res_1.5/vpv/train/

Remote Sensing Bio‑Digester DatasetOverviewTo the best of our knowledge, this is the first large-scale satellite dataset of bio‑digesters, with facility‑level and part‑level annotations. It comprises high‑resolution aerial and satellite imagery of bio‑digester sites across France’s Grand Est region, drawn from multiple sources. Ground‑truth labels are geolocated segmentation masks for three classes:  Whole installation (entire bio‑digester site)  Anaerobic digestion tanks (internal tanks)  Biomass piles (feedstock storage areas)Code available On GithubCompositionTraining & validation sets include SPOT, Sentinel, and aerial modalities. Training is made easy using MMRotate as annotations are also available in DOTA format.Resolutions: 0.5 m, 1.5 m, and 5 m per pixel.  Modalities: Aerial, SPOT, and Sentinel imagery—to study transferability and resolution impact.  Labels remain geolocated; due to modality mismatches, some labels transfer poorly, so aerial annotations serve as the definitive reference. Coordinates provided in EPSG:2154.Directory Structure├── README.md├── res_0.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   └── vpv│       ├── train│       └── val├── res_1.5│   ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val│   ├── label│   │   ├── train│   │   └── val│   ├── test│   │   ├── image│   │   ├── label│   │   └── meta│   └── vpv│       ├── train│       └── val└── res_5    ├── image│   │   ├── BDORTHO│   │   │   ├── train│   │   │   └── val│   │   ├── SENTINEL│   │   │   ├── train│   │   │   └── val│   │   └── SPOT│   │       ├── train│   │       └── val    ├── label    │   ├── train    │   └── val    └── vpv        ├── train        └── valTestingTest SplitCovers the entire French department of Marne, pre‑tiled at 1.5 m resolution in 1000×1000 px tiles (~5000 tiles).Environment & ModalitiesAvailable only in aerial and SPOT modalities at 1.5 m resolution.A precomputed tiling facilitates large‑scale evaluation without engineering overhead.Objective & Metrics- Detect all bio‑digester sites within 200 m accuracy (AP@200 m as introduced in the paper).  - Augmented with newly discovered Marne‑region digesters to better estimate precision at scale:    - SPOT: 29 sites    - Aerial: 27 sites (including 4 under construction)  VisualizationOptionally, use vpv to explore the dataset:vpv ac aw nw ./res_1.5/image/BDORTHO/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/PLEIADES/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SPOT/train/ svg:./res_1.5/vpv/train/ \    nw ./res_1.5/image/SENTINEL/train/ svg:./res_1.5/vpv/train/

Code and data to run the digital twin of an X-ray Raman imaging experiment of a radiation-sensitive sample.

Code and data to run the digital twin of an X-ray Raman imaging experiment of a radiation-sensitive sample.

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In 2023, an unprecedented marine heatwave (MHW) developed in the North Atlantic. MHWs have severe ecological and socioeconomic impacts, and their increasing frequency and intensity demand urgent action from climate scientists and policymakers. The characterisation of MHWs requires high-resolution observations not only of ocean temperature, but also of its physical drivers, such as wind and ocean waves. However, acquiring co-located, in-situ measurements of these variables remains logistically challenging, and data scarcity continues to hinder efforts to fully understand and model MHW dynamics. Here, we present a unique dataset collected by a freely drifting buoy off the west coast of Ireland during the peak of the 2023 MHW event. The dataset includes 1-minute sea surface temperature (SST) and position records, directional wave spectra, wind speed estimates, and derived wave parameters. These data provide an unique opportunity to analyse air–sea interactions during a MHW at fine temporal scales. They are intended to support coupled model validation, diurnal warming studies, and data assimilation efforts, ultimately contributing to improved understanding and forecasting of MHW.