Spatial AI
GeoSR-Bench
GeoSR-Bench
Dataset and model weights for the paper:
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration [arXiv]
The code is available on GitHub: https://github.com/ai-spatial/GeoSR-Bench
Dataset Description
GeoSR-Bench directly connects super-resolution (SR) with downstream Earth monitoring tasks, moving beyond conventional fidelity-based evaluation. It comprises spatially co-located… See the full description on the dataset page: https://huggingface.co/datasets/ai-spatial/GeoSR-Bench.ai2thor_spatial_verification_val_v2ai2thor_spatial_verification_test_v2CarbonGlobe
CarbonGlobe: A Global-Scale, Multi-Decade Dataset and Benchmark for Carbon Forecasting in Forest Ecosystems
CarbonGlobe is a global-scale, multi-decade, machine-learning-ready dataset and benchmark for forecasting carbon dynamics in forest ecosystems. The dataset provides harmonized environmental drivers and carbon-related ecosystem outputs simulated by the Ecosystem Demography model version 3 (ED v3), enabling the development, evaluation, and comparison of deep learning models… See the full description on the dataset page: https://huggingface.co/datasets/ai-spatial/CarbonGlobe.DERE
DERE Dataset
DERE is a multi-source ecosystem dataset for global carbon-flux prediction. It
integrates Ecosystem Demography (ED) simulations, ED-derived vegetation
structure, ESA CCI plant functional type fractions, LiDAR-derived forest-age
information, and real-world in-situ carbon-flux observations.
The dataset is organized into two complementary collections. GlobalMask
provides globally sampled simulation and remote-sensing data, while
InSituMatched links the same… See the full description on the dataset page: https://huggingface.co/datasets/ai-spatial/DERE.sea-small
Spatial Everyday Activities
[Website] [Contact]
Spatial Everyday Activities (SEA) is an egocentric dataset designed for training robotic foundation models. It comprises approximately 10,000 hours of egocentric data collected by computer vision experts across a diverse range of locations in the US and EU. SEA-small is a 100GB open-source subset of the full SEA dataset.
info@spatial-ai.com
Run the code
Setup an isolated environment
conda create -n sea python=3.12
conda… See the full description on the dataset page: https://huggingface.co/datasets/spatial-ai/sea-small.