datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GeoGrid_Bench
GeoGrid-Bench: Can Foundation Models Understand Multimodal Gridded Geo-Spatial Data?
We present GeoGrid-Bench, a benchmark designed to evaluate the ability of foundation models to understand geo-spatial data in the grid structure. Geo-spatial datasets pose distinct challenges due to their dense numerical values, strong spatial and temporal dependencies, and unique multimodal representations including tabular data, heatmaps, and geographic visualizations. To assess how foundation… See the full description on the dataset page: https://huggingface.co/datasets/bowen-upenn/GeoGrid_Bench.geotree
Tree Monitoring Dataset - Bangladesh
This dataset is prepared for training object detection and segmentation models to monitor tree canopies in Bangladesh.
Dataset Structure
sentinel2/: Raw Sentinel-2 Level-2A imagery for Bandarban, Rangamati, Sylhet, and Gazipur districts.
deepforest/: DeepForest annotations and tree crown samples.
zenodo/: Reference training datasets.
selvabox/: Tree canopy labels and annotations.
global_forest_change/: Hansen Global Forest… See the full description on the dataset page: https://huggingface.co/datasets/the-shoaib2/geotree.geolayers
Geolayers-Data
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This dataset card contains usage instructions and metadata for all data-products released with our paper:Using Multiple Input Modalities can Improve Data-Efficiency and O.O.D. Generalization for ML with Satellite Imagery. We release 3 modified versions of 3 benchmark datasets spanning land-cover segmentation, tree-cover regression, and multi-label land-cover classification tasks. These datasets are augmented with auxiliary, geographic inputs. A full list of… See the full description on the dataset page: https://huggingface.co/datasets/arjunrao2000/geolayers.xinyi_geodata