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Publication Year: 2025

11 to 20 of 1,822 Results

bupst20_rgb.tar.gz.ac Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data application/pkix-attr-cert - 4.0 GB - MD5: 93338ef9591e503e1dec8da6b604e2f9
bupst20_rgb.tar.gz.ad Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data Unknown - 4.0 GB - MD5: 6e19dd2a7a0444d63ee08e473a84301d
bupst20_rgb.tar.gz.ae Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data Unknown - 4.0 GB - MD5: 74b70f880f50c033bf2971013079144a
bupst20_rgb.tar.gz.af Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data Unknown - 4.0 GB - MD5: 252b920bac08bc0930a21a770569a738
bupst20_rgb.tar.gz.ag Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data image/x-applix-graphics - 3.5 GB - MD5: abd54e3866adab851cb575d1d3a58d36
cam_params.yaml Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data application/x-yaml - 280 B - MD5: ed212b2055b08b635f263320a42dec15
dataset_structure.md Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data Markdown Text - 2.9 KB - MD5: aa1af827322e4ae5dc3f0dc668af7562
how_to_use_BUPST20.md Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data Markdown Text - 5.1 KB - MD5: 30831e1ba9bab6fbaa91fd347ce5d855
train_valid_eval_splits.yaml Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data application/x-yaml - 1.6 KB - MD5: dcf653a6d147f9c6eb577b7f65ce000a
RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set] Oct 1, 2025 Shams Eddin, Mohamad Hakam; Zhang, Yikui; Kollet, Stefan; Gall, Juergen, 2025, "RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]", https://doi.org/10.60507/FK2/T8QYWE, bonndata, V1 This is the dataset used in the RiverMamba paper (see https://arxiv.org/abs/2505.22535). The aim of the RiverMamba project is to develop a deep learning model that is pretrained with long-term reanalysis data and fine-tuned on observations to forecast global river discharge and floods up to 7 days lead time on a 0.05° grid. The dataset includes the...

bupst20_rgb.tar.gz.ac

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

application/pkix-attr-cert - 4.0 GB -

bupst20_rgb.tar.gz.ad

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

Unknown - 4.0 GB -

bupst20_rgb.tar.gz.ae

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

Unknown - 4.0 GB -

bupst20_rgb.tar.gz.af

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

Unknown - 4.0 GB -

bupst20_rgb.tar.gz.ag

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

image/x-applix-graphics - 3.5 GB -

cam_params.yaml

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

application/x-yaml - 280 B -

dataset_structure.md

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

Markdown Text - 2.9 KB -

how_to_use_BUPST20.md

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

Markdown Text - 5.1 KB -

train_valid_eval_splits.yaml

Oct 6, 2025 - BUP-ST20: Weakly Labelled Spatial Temporal Sweet Pepper Data

application/x-yaml - 1.6 KB -

RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]

Oct 1, 2025

Shams Eddin, Mohamad Hakam; Zhang, Yikui; Kollet, Stefan; Gall, Juergen, 2025, "RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting [data set]", https://doi.org/10.60507/FK2/T8QYWE, bonndata, V1

This is the dataset used in the RiverMamba paper (see https://arxiv.org/abs/2505.22535). The aim of the RiverMamba project is to develop a deep learning model that is pretrained with long-term reanalysis data and fine-tuned on observations to forecast global river discharge and floods up to 7 days lead time on a 0.05° grid. The dataset includes the...

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