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Using image segmentation models to analyse high-resolution earth observation data: new tools to monitor disease risks in changing environments. [PDF]
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Enhancing Earth Observation Throughput Using Inter-Satellite Communication
IEEE Transactions on Wireless Communications, 2022Earth observation systems play important roles in many critical applications. The rapid increase of the number of satellites and their sensing capability, however, makes it challenging to send the massive amount of observed data back to the Earth.
Peng Wang, Shun Zhang, Binbin Chen
exaly +2 more sources
IEEE Geoscience and Remote Sensing Magazine, 2023
Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation.
Yi Wang +5 more
semanticscholar +1 more source
Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation.
Yi Wang +5 more
semanticscholar +1 more source
Earth Observation Satellite Management
Constraints, 1999The daily management of an earth observation satellite is a challenging combinatorial optimization problem. This problem can be roughly stated as follows: given (1) a set of candidate images for the next day, each one associated with a weight reflecting its importance, (2) a set of imperative constraints expressing physical limitations (no overlapping ...
E. Bensana +2 more
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A dynamic earth observation system
Parallel Computing, 2003The paper presents an overview of SARA/Digital Puglia (Synthetic Aperture Radar Atlas), a remote sensing environment that shows how grid technologies and high performance computing can be efficiently used to build dynamic earth observation systems for the management of huge quantities of data coming from space missions and for their on-demand ...
ALOISIO, Giovanni, CAFARO, Massimo
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TerraMind: Large-Scale Generative Multimodality for Earth Observation
IEEE International Conference on Computer VisionWe present TerraMind, the first any-to-any generative, multimodal deep learning model for Earth observation (EO). Unlike other approaches, TerraMind is pretrained on dualscale representations combining both token-level and pixellevel data across ...
Johannes Jakubik +15 more
semanticscholar +1 more source
RemoteSAM: Towards Segment Anything for Earth Observation
ACM MultimediaWe aim to develop a robust yet flexible visual foundation model for Earth observation. It should possess strong capabilities in recognizing and localizing diverse visual targets while providing compatibility with various input-output interfaces required ...
Liang Yao +8 more
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Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents
arXiv.orgEarth observation (EO) is essential for understanding the evolving states of the Earth system. Although recent MLLMs have advanced EO research, they still lack the capability to tackle complex tasks that require multi-step reasoning and the use of domain-
Pei-Lin Feng +10 more
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OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation
arXiv.orgEarth observation data presents a unique challenge: it is spatial like images, sequential like video or text, and highly multimodal. We present OlmoEarth: a multimodal, spatio-temporal foundation model that employs a novel self-supervised learning ...
Henry Herzog +25 more
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