$A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder$

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A $^{2}$ -MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder

12 June 2024

Lixian Zhang

Weijia Li

Wayne Zhang

ArXiv (abs)PDF HTML Github

Papers citing "A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder"

6 / 6 papers shown

MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Data

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TESSERA: Precomputed FAIR Global Pixel Embeddings for Earth Representation and Analysis

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TiMo: Spatiotemporal Foundation Model for Satellite Image Time Series

446

13 May 2025

A comprehensive review of remote sensing in wetland classification and mapping

345

15 Apr 2025

A Survey on Remote Sensing Foundation Models: From Vision to Multimodality

526

28 Mar 2025

Towards a Unified Copernicus Foundation Model for Earth Vision

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611

14 Mar 2025

A2^{2}2-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder

Papers citing "A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder"

A2^{2}2-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder

Papers citing "A$^{2}$-MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder"

A $^{2}$ -MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder

A $^{2}$ -MAE: A spatial-temporal-spectral unified remote sensing pre-training method based on anchor-aware masked autoencoder