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EarthNets: Empowering AI in Earth Observation
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Earth observation (EO), aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With a growing number of satellites in orbit, an increasing number of datasets with diverse sensors and research domains are being published to facilitate the research of the remote sensing community. This paper presents a comprehensive review of more than 500 publicly published datasets, including research domains like agriculture, land use and land cover, disaster monitoring, scene understanding, vision-language models, foundation models, climate change, and weather forecasting. We systematically analyze these EO datasets from four aspects: volume, resolution distributions, research domains, and the correlation between datasets. Based on the dataset attributes, we propose to measure, rank, and select datasets to build a new benchmark for model evaluation. Furthermore, a new platform for EO, termed EarthNets, is released to achieve a fair and consistent evaluation of deep learning methods on remote sensing data. EarthNets supports standard dataset libraries and cutting-edge deep learning models to bridge the gap between the remote sensing and machine learning communities. Based on this platform, extensive deep-learning methods are evaluated on the new benchmark. The insightful results are beneficial to future research. The platform and dataset collections are publicly available at https://earthnets.github.io.
Forward citations
Cited by 7 Pith papers
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EarthShift: a benchmark for measuring robustness to real-world distribution shifts in Earth observation
EarthShift is a new benchmark using paired datasets to measure robustness of geospatial foundation models to realistic distribution shifts, finding consistent 15-20% performance drops out-of-distribution across 8 mode...
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OVEarth-Bench: Evaluating Category Breadth and Query Diversity for Open-Vocabulary Earth Observation
A new 172-category, three-query-form Earth observation benchmark shows the best open-vocabulary model reaches only 38.75% mean IoU, with MLLM-based systems leading and EO-specific models trailing.
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OVEarth-Bench: Evaluating Category Breadth and Query Diversity for Open-Vocabulary Earth Observation
OVEarth-Bench, a new open-vocabulary Earth observation benchmark with broad category coverage and diverse queries, shows MLLM-based methods outperform EO-specific ones.
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How to Embed Matters: Evaluation of EO Embedding Design Choices
Transformer backbones with mean pooling and combined self-supervised embeddings yield robust, compact representations for EO tasks that are over 500x smaller than raw data.
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MSAM: Multi-Semantic Adaptive Mining for Cross-Modal Drone Video-Text Retrieval
MSAM introduces two drone-video/text datasets and a CLIP-based multi-semantic pooling model that reports 0.6–3.8 point R@1 gains over earlier video-text retrieval methods.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithos...
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
A review of Earth science foundation models covering capability evolution from perception to discovery, applications across atmosphere/hydrosphere/lithosphere/biosphere/anthroposphere/cryosphere, over 200 datasets, an...
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