Multi-modal remote sensing large foundation models: current research status and future prospect

doi:10.11947/j.AGCS.2024.20240019.

Abstract

Abstract:

The increasing remote sensing capabilities for Earth observation have eased the access to abundant data and enabled the emergence and development of remote sensing foundation models (RSFMs). Designing distinct deep neural networks and optimizing for different data and task types require substantial development efforts and prohibitively high computational resources. In order to address these issues, researchers in the remote sensing field have shifted their focus to the study of RSFMs and presented many dedicated designed unified models. To enhance the generalizability and interpretability of RSFMs, the integration of extensive geographic knowledge has been recognized as a pivotal/key approach. While existing works have explored or incorporated geographic knowledge into the architecture design or pre-training methods of RSFMs, there lacks of a comprehensive survey to review the current status of geographic knowledge-guided RSFMs. Therefore, this paper starts with summarizing and categorizing large-scale pre-training datasets and then provides an overview of the research progress in this field. Subsequently, we introduce intelligent interpretation algorithms for remote sensing imagery guided by geographic knowledge, along with advancements in the exploration and utilization of geographic knowledge specifically tailored for RSFMs. Finally, several future research prospects are outlined to tackle the persisting challenges in this field, aiming to shed light on future investigations into RSFMs.

Key words: pre-training dataset, remote sensing intelligent interpretation, remote sensing foundation models, geographic knowledge

CLC Number:

P237

Yongjun ZHANG, Yansheng LI, Bo DANG, Kang WU, Xin GUO, Jian WANG, Jingdong CHEN, Ming YANG. Multi-modal remote sensing large foundation models: current research status and future prospect[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(10): 1942-1954.

Figures/Tables 4

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Fig.1

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数据集	图像数量	图像大小/像素	空间分辨率/m	图像类型	图像数据源	覆盖地理位置
fMoW^[24]	1 047 691	—	—	多光谱(4/8波段)	Digital Globe	全球
SEN12MS^[27]	180 662	256	10	合成孔径雷达-多光谱	哨兵1号、哨兵2号	全球
BigEarthNet-MM^[25]	1 180 652	20~120	10~60	合成孔径雷达-多光谱	哨兵1号、哨兵2号	欧洲
MillionAID^[22]	1 000 848	110~31 672	0.5~153	可见光	Google Earth	—
SeCo^[12]	1 000 000	—	10	多光谱	哨兵2号	全球
fMoW-Sentinel^[28]	882 779	45~60	10	多光谱(13波段)	哨兵2号	全球
TOV-RS-Balanced^[20]	500 000	600	1~20	可见光	Google Earth	-
SSL4EO-S12^[26]	3 012 948	20~120	10~60	合成孔径雷达-多光谱	哨兵1号、哨兵2号	全球
SSL4EO-L^[29]	5 000 000	264	30	多光谱	Landsat4-5，7-9	全球
SatlasPretrain^[23]	856 000	512	0.5~2，10	可见光&多光谱	NAIP、哨兵2号	全球

数据集	数量	属性
RSICD^[30]	24 333个文本描述、10 921张遥感影像	图像-文本描述
RSITMD^[31]	23 715个文本描述、4743张遥感影像	图像-文本描述
RSVGD^[32]	38 320个语言表达、17 402张遥感影像	视觉定位
RS5M^[33]	500万个图像文本对	图像-文本描述
RSICap^[34]	2585个图像文本对	图像-文本描述
文献[35]	828 725个图像文本对	图像-文本描述
文献[36]	318 000个图像指令提示对	图像-文本描述、定位描述、区域描述、复杂对话