
浏览全部资源
扫码关注微信
武汉大学测绘遥感信息工程全国重点实验室,湖北 武汉,430079
Received:28 September 2026,
Published:05 November 2026
移动端阅览
钟燕飞,万瑜廷,马爱龙,等.高分辨率多模态遥感智能解译进展与展望:从遥感基础模型到遥感智能解译脑[J].武汉大学学报(信息科学版),2026,51(11):2329-2355.
ZHONG Yanfei,WAN Yuting,MA Ailong,et al.Progress and Prospects in High⁃Resolution Multimodal Remote Sensing Intelligent Interpretation: From Remote Sensing Foundation Models to the Remote Sensing Intelligent Interpretation Brain[J].Geomatics and Information Science of Wuhan University,2026,51(11):2329-2355.
钟燕飞,万瑜廷,马爱龙,等.高分辨率多模态遥感智能解译进展与展望:从遥感基础模型到遥感智能解译脑[J].武汉大学学报(信息科学版),2026,51(11):2329-2355. DOI:10.13203/j.whugis20260400
ZHONG Yanfei,WAN Yuting,MA Ailong,et al.Progress and Prospects in High⁃Resolution Multimodal Remote Sensing Intelligent Interpretation: From Remote Sensing Foundation Models to the Remote Sensing Intelligent Interpretation Brain[J].Geomatics and Information Science of Wuhan University,2026,51(11):2329-2355. DOI:10.13203/j.whugis20260400
高分辨率多模态遥感智能解译是实现复杂地学场景感知与理解的重要手段,在地表动态监测、资源环境调查和灾害应急等领域具有重要应用价值。近年来,随着深度学习与基础模型的发展,遥感智能解译在多模态特征感知、语义理解认知和复杂决策服务等方面取得了显著进展。系统梳理了遥感影像基础模型、遥感理解基础模型与遥感决策服务平台的研究进展,并在此基础上提出从遥感基础模型向遥感智能解译脑演进的发展思路。同时,分析了当前高分辨率多模态遥感智能解译面临的多模态统一表征不足、复杂地学知识推理可信性有限、感知认知与决策执行协同不足等关键挑战,并从空天地人一体化感知、多模态多尺度统一表征、知识引导的可信地学认知、线上实时自主决策、软硬一体化系统集成与应用等方面展望未来发展方向,以期为高分辨率多模态遥感智能解译的理论研究与技术发展提供参考。
High-resolution multimodal remote sensing intelligent interpretation is an important means of perceiving and understanding complex geospatial scenes
with significant applications in dynamic land surface monitoring
natural resource and environmental surveys
and disaster emergency response. In recent years
advances in deep learning and foundation models have substantially improved remote sensing intelligent interpretation in multimodal feature perception
semantic understanding and cognition
and complex decision-making services. This paper systematically reviews the research progress in remote sensing image foundation models
remote sensing understanding foundation models
and remote sensing decision-making service platforms
and proposes an evolutionary pathway from remote sensing foundation models toward a remote sensing intelligent interpretation brain. Furthermore
it analyzes the key challenges facing high-resolution multimodal remote sensing intelligent interpretation
including insufficient unified multimodal representation
limited reliability of complex geoscientific knowledge reasoning
and inadequate coordination among perception
cognition
decision-making
and execution. Finally
future research directions are discussed in terms of integrated space-air-ground-human perception
unified multimodal and multiscale representation
knowledge-guided trustworthy geoscientific cognition
online real-time autonomous decision-making
and integrated software-hardware systems and applications. This review aims to provide a reference for theoretical research and technological development in high-resolution multimodal remote sensing intelligent interpretation.
REICHSTEIN M , CAMPS-VALLS G , STEVENS B , et al . Deep Learning and Process Understanding for Data-Driven Earth System Science [J]. Nature , 2019 , 566 ( 7743 ): 195 - 204 .
HANAN N P , ANCHANG J Y . Satellites Could soon Map Every Tree on Earth [J]. Nature , 2020 , 587 ( 7832 ): 42 - 43 .
VOIGT S , GIULIO-TONOLO F , LYONS J , et al . Global Trends in Satellite-Based Emergency Mapping [J]. Science , 2016 , 353 ( 6296 ): 247 - 252 .
ZHU X X , TUIA D , MOU L C , et al . Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources [J]. IEEE Geoscience and Remote Sensing Magazine , 2017 , 5 ( 4 ): 8 - 36 .
ZHANG L P , ZHANG L F , DU B . Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art [J]. IEEE Geoscience and Remote Sensing Magazine , 2016 , 4 ( 2 ): 22 - 40 .
BREIMAN L . Random Forests [J]. Machine Learning , 2001 , 45 ( 1 ): 5 - 32 .
KRIZHEVSKY A , SUTSKEVER I , HINTON G E . ImageNet Classification with Deep Convolutional Neural Networks [J]. Communications of the ACM , 2017 , 60 ( 6 ): 84 - 90 .
OUYANG L , WU J , XU J , et al . Training Language Models to Follow Instructions with Human Feedback [C]// International Conference on Neural Information Processing Systems , New Orleans, Louisiana, USA,2022 .
BOMMASANI R , HUDSON D A , ADELI E , et al . On the Opportunities and Risks of Foundation Models [PP/OL]. V3. arXiv ( 2022-07-12 )[ 2026-10-11 ]. https://doi.org/10.48550/arXiv.2108.07258 https://doi.org/10.48550/arXiv.2108.07258 .
BROWN T B , MANN B , RYDER N , et al . Language Models Are Few-Shot Learners [C]// International Conference on Neural Information Processing Systems , Red Hook,NY,USA, 2020 .
CONG Y Z , KHANNA S , MENG C L , et al . SatMAE: Pre-Training Transformers for Temporal and Multi-Spectral Satellite Imagery [C]// Advances in Neural Information Processing Systems 35 , New Orleans, Louisiana, USA, 2022 .
SUN X , WANG P J , LU W X , et al . RingMo: A Remote Sensing Foundation Model With Masked Image Modeling [J]. IEEE Transactions on Geoscience and Remote Sensing , 2023 , 61 : 5612822 .
KUCKREJA K , DANISH M S , NASEER M , et al . GeoChat: Grounded Large Vision-Language Model for Remote Sensing [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Seattle, WA, USA, 2024 .
WAN Y T , ZHANG C , MA A L , et al . Remote Sensing Intelligent Interpretation Brain: Real-Time Intelligent Understanding of the Earth [J]. PNAS Nexus , 2025 , 4 ( 6 ): pgaf182 .
MENDIETA M , HAN B R , SHI X J , et al . Towards Geospatial Foundation Models via Continual Pretraining [C]// IEEE/CVF International Conference on Computer Vision (ICCV) , Paris, France , 2023 .
REED C J , GUPTA R , LI S F , et al . Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning [C]// IEEE/CVF International Conference on Computer Vision (ICCV) , Paris, France , 2023 .
IBANEZ D , FERNANDEZ-BELTRAN R , PLA F , et al . Masked Auto-Encoding Spectral–Spatial Transformer for Hyperspectral Image Classification [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 1 - 14 .
JAKUBIK J , ROY S , PHILLIPS C E , et al . Foundation Models for Generalist Geospatial Artificial Intelligence [PP/OL]. V2. arXiv ( 2023-11-08 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2310.18660 https://doi.org/10.48550/arXiv.2310.18660 .
BASTANI F , WOLTERS P , GUPTA R , et al . SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding [C]// IEEE/CVF International Conference on Computer Vision (ICCV) , Paris, France , 2023 .
IRVIN J , TAO L , ZHOU J , et al . USat: A Unified Self-Supervised Encoder for Multi-Sensor Satellite Imagery [PP/OL]. V1.arXiv ( 2023-12-02 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2312.02199 https://doi.org/10.48550/arXiv.2312.02199 .
WANG Y , ALBRECHT C M , ALI BRAHAM N A , et al . Decoupling Common and Unique Representations for Multimodal Self-Supervised Learning [C]// European Conference—ECCV 2024 , Milan, Italy , 2025 .
GUO X , LAO J W , DANG B , et al . SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Seattle, WA, USA, 2024 .
FULLER A , MILLARD K , GREEN J . CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders [C]// Advances in Neural Information Processing Systems 36 , New Orleans, Louisiana, USA, 2023 .
LI X Y , HONG D F , CHANUSSOT J . S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Seattle, WA, USA, 2024 .
NOMAN M , NASEER M , CHOLAKKAL H , et al . Rethinking Transformers Pre-Training for Multi-spectral Satellite Imagery [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Seattle, WA, USA, 2024 .
LI J T , LIU Y Y , WANG X Y , et al . HyperFree: A Channel-Adaptive and Tuning-Free Foundation Model for Hyperspectral Remote Sensing Imagery [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Nashville, TN, USA, 2025 .
LI Z H , HOU B , MA S T , et al . Masked Angle-Aware Autoencoder for Remote Sensing Images [C]// The 18th European Conference—ECCV 2024 , Milan, Italy , 2025 .
TANG M F , COZMA A , GEORGIOU K , et al . Cross-Scale MAE: A Tale of Multiscale Exploitation in Remote Sensing [C]// Advances in Neural Information Processing Systems 36 , New Orleans, Louisiana, USA, 2023 .
JIANG W T , ZHANG J , WANG D , et al . LeMeViT: Efficient Vision Transformer with Learnable Meta Tokens for Remote Sensing Image Interpretation [C]// The 33rd International Joint Conference on Artificial Intelligence , Jeju, South Korea , 2024 .
WANYAN X , SENEVIRATNE S , SHEN S C , et al . Extending Global-Local View Alignment for Self-Supervised Learning with Remote Sensing Imagery [C]// 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , Seattle, WA, USA, 2024 .
ZHANG X , LIU Y , LIN Y M , et al . UV-SAM: Adapting Segment Anything Model for Urban Village Identification [C]// The AAAI Conference on Artificial Intelligence , Vancouver, Canada , 2024 .
LI W J , YANG W , HOU Y N , et al . SARATR-X: Toward Building a Foundation Model for SAR Target Recognition [J]. IEEE Transactions on Image Processing , 2025 , 34 : 869 - 884 .
XIONG Z T , WANG Y , ZHANG F H , et al . Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation [PP/OL]. V3. arXiv ( 2025-10-15 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2403.15356 https://doi.org/10.48550/arXiv.2403.15356 .
ASTRUC G , GONTHIER N , MALLET C , et al . OmniSat: Self-Supervised Modality Fusion for Earth Observation [C]// European Conference—ECCV 2024 , Milan, Italy , 2025 .
LI W J , YANG W , LIU T P , et al . Predicting Gradient Is Better: Exploring Self-Supervised Learning for SAR ATR with a Joint-Embedding Predictive Architecture [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2024 , 218 : 326 - 338 .
GONG Z M , WANG A T , HUO X L , et al . CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale [PP/OL]. V5. arXiv ( 2025-12-08 )[ 2026-10-11 ]. https://doi.org/10.48550/arXiv.2405.17537 https://doi.org/10.48550/arXiv.2405.17537 .
WANG D , HU M Q , JIN Y , et al . HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2025 , 47 ( 8 ): 6427 - 6444 .
WANG F X , WANG H Z , WANG D , et al . Harnessing Massive Satellite Imagery with Efficient Masked Image Modeling [C]// IEEE/CVF International Conference on Computer Vision (ICCV) , Honolulu, HI, USA, 2025 .
DIAO W H , YU H C , KANG K Y , et al . RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning [PP/OL]. V4. arXiv ( 2025-09-16 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2409.13366 https://doi.org/10.48550/arXiv.2409.13366 .
SIMÉONI O , VO H V , SEITZER M , et al . DINOv3 [PP/OL]. V1.arXiv ( 2025-08-13 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2508.10104 https://doi.org/10.48550/arXiv.2508.10104 .
ZHANG S , WANG Q Z , LIU J Y , et al . ALPS: An Auto-Labeling and Pre-Training Scheme for Remote Sensing Segmentation With Segment Anything Model [J]. IEEE Transactions on Image Processing , 2025 , 34 : 2408 - 2420 .
TRANTAS A , MENSIO M , STASINOS S , et al . BioAnalyst: A Foundation Model for Biodiversity [PP/OL]. V2. arXiv ( 2025-12-04 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2507.09080 https://doi.org/10.48550/arXiv.2507.09080 .
BROWN C F , KAZMIERSKI M R , PASQUARELLA V J , et al . AlphaEarth Foundations: An Embedding Field Model for Accurate and Efficient Global Mapping from Sparse Label Data [PP/OL]. V2.arXiv ( 2025-09-08 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2507.22291 https://doi.org/10.48550/arXiv.2507.22291 .
ASTRUC G , GONTHIER N , MALLET C , et al . AnySat: One Earth Observation Model for Many Resolutions, Scales, and Modalities [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Nashville, TN, USA, 2025 .
LI X Y , LI C Y , VIVONE G , et al . SeaMo: A Season-Aware Multimodal Foundation Model for Remote Sensing [J ]. Information Fusion , 2026 , 125 : 103334 .
HERZOG H , BASTANI F , ZHANG Y W , et al . OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation [PP/OL]. V1.arXiv ( 2025-11-17 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2511.13655 https://doi.org/10.48550/arXiv.2511.13655 .
BI H B , FENG Y C , TONG B Y , et al . RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2026 , 48 ( 4 ): 4388 - 4405 .
SASTRY S , KHANAL S , DHAKAL A , et al . TaxaBind: A Unified Embedding Space for Ecological Applications [C]//IEEE/CVF Winter Conferen‑ ce on Applications of Computer Vision (WACV) ,Tucson, AZ, USA , 2025 .
SZWARCMAN D , ROY S , FRACCARO P , et al . Prithvi-EO-2 . 0 : A Versatile Multi-Temporal Foundation Model for Earth Observation Applications[PP/OL]. V3. arXiv ( 2026-03-06 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2412.02732 https://doi.org/10.48550/arXiv.2412.02732 .
FENG Z , ATZBERGER C , JAFFER S , et al . TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysis [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition , Denver,Colorado,USA, 2026 .
YANG R , LIU Y , YAN H , et al . MaRS: A Multi-modality Very-High-Resolution Remote Sensing Foundation Model with Cross-Granularity Meta-Modality Learning [C]// The AAAI Conference on Artificial Intelligence , Vancouver, British Columbia, Canada, 2026 .
HONG D F , ZHANG B , LI X Y , et al . SpectralGPT: Spectral Remote Sensing Foundation Model [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024 , 46 ( 8 ): 5227 - 5244 .
WU K , ZHANG Y Y , RU L X , et al . A Semantic-Enhanced Multi-Modal Remote Sensing Foundation Model for Earth Observation [J]. Nature Machine Intelligence , 2025 , 7 ( 8 ): 1235 - 1249 .
QU B , LI X L , TAO D C , et al . Deep Semantic Understanding of High Resolution Remote Sensing Image [C]// International Conference on Computer, Information and Telecommunication Systems (CITS) , Kunming, China , 2016 .
LU X Q , WANG B Q , ZHENG X T , et al . Exploring Models and Data for Remote Sensing Image Caption Generation [J]. IEEE Transactions on Geoscience and Remote Sensing , 2018 , 56 ( 4 ): 2183 - 2195 .
CHENG Q M , HUANG H Y , XU Y , et al . NWPU-Captions Dataset and MLCA-Net for Remote Sensing Image Captioning [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5629419 .
HU Y , YUAN J L , WEN C C , et al . RSGPT: A Remote Sensing Vision Language Model and Benchmark [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2025 , 224 : 272 - 286 .
LOBRY S , MARCOS D , MURRAY J , et al . RSVQA: Visual Question Answering for Remote Sensing Data [J]. IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 12 ): 8555 - 8566 .
ZHENG X T , WANG B Q , DU X Q , et al . Mutual Attention Inception Network for Remote Sensing Visual Question Answering [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5606514 .
LOBRY S , DEMIR B , TUIA D . RSVQA Meets Bigearthnet: A New, Large-Scale, Visual Question Answering Dataset for Remote Sensing [C]// IEEE International Geoscience and Remote Sensing Symposium IGARSS , Brussels, Belgium , 2021 .
RAHNEMOONFAR M , CHOWDHURY T , SARKAR A , et al . FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding [J]. IEEE Access , 2021 , 9 : 89644 - 89654 .
LI K , VOSSELMAN G , YANG M Y . HRVQA: A Visual Question Answering Benchmark for High-Resolution Aerial Images [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2024 , 214 : 65 - 81 .
WANG J J , ZHENG Z , CHEN Z H , et al . EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answering [C]// The AAAI Conference on Artificial Intelligence , Vancouver, Canada , 2024 .
YUAN Z Q , ZHANG W K , FU K , et al . Exploring a Fine-Grained Multiscale Method for Cross-Modal Remote Sensing Image Retrieval [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 4404119 .
ZHAN Y , XIONG Z T , YUAN Y . RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Data [J]. IEEE Transactions on Geoscience and Remote Sensing , 2023 , 61 : 5604513 .
YUAN Z H , MOU L C , HUA Y S , et al . RRSIS: Referring Remote Sensing Image Segmentation [J]. IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5613312 .
LIU S H , MA Y W , ZHANG X Q , et al . Rotated Multi-scale Interaction Network for Referring Remote Sensing Image Segmentation [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Seattle, WA, USA, 2024 .
LIU C Y , ZHAO R , CHEN H , et al . Remote Sensing Image Change Captioning With Dual-Branch Transformers: A New Method and a Large Scale Dataset [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5633520 .
YUAN Z H , MOU L C , XIONG Z T , et al . Change Detection Meets Visual Question Answering [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5630613 .
KARACA A C , OZELBAS E , BERBER S , et al . Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and MModalCC Framework [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2025 , 18 : 21494 - 21513 .
GAJBHIYE G O , NANDEDKAR A V . Generating the Captions for Remote Sensing Images: A Spatial-Channel Attention Based Memory-Guided Transformer Approach [J]. Engineering Applications of Artificial Intelligence , 2022 , 114 : 105076 .
ZHAO K , XIONG W . Region-Guided Transformer for Remote Sensing Image Captioning [J]. International Journal of Digital Earth , 2024 , 17 ( 1 ): 2400988 .
ABDULLAH T , BAZI Y , AL RAHHAL M M , et al . TextRS: Deep Bidirectional Triplet Network for Matching Text to Remote Sensing Images [J]. Remote Sensing , 2020 , 12 ( 3 ): 405 .
CHENG Q M , ZHOU Y Z , FU P , et al . A Deep Semantic Alignment Network for the Cross-Modal Image-Text Retrieval in Remote Sensing [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 2021 , 14 : 4284 - 4297 .
HE Y P , XU X , CHEN H J , et al . Visual Global-Salient-Guided Network for Remote Sensing Image-Text Retrieval [J]. IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5641814 .
ZHANG Z L , ZHAO T C , GUO Y L , et al . RS5M and GeoRSCLIP: A Large-Scale Vision- Language Dataset and a Large Vision-Language Model for Remote Sensing [J]. IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5642123 .
LIU F , CHEN D L , GUAN Z , et al . RemoteCLIP: A Vision Language Foundation Model for Remote Sensing [J]. IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5622216 .
WANG Z C , PRABHA R , HUANG T Y , et al . SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing [C]// The AAAI Conference on Artificial Intelligence , Vancouver, Canada , 2024 .
MALL U , PHOO C P , LIU M K , et al . Remote Sensing Vision-Language Foundation Models Without Annotations via Ground Remote Alignment [C]// International Conference on Learning Representations , Vienna, Austria , 2024 .
DHAKAL A , AHMAD A , KHANAL S , et al . Sat2Cap: Mapping Fine-Grained Textual Descriptions from Satellite Images [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , Seattle, WA, USA, 2024 .
JAIN P , IENCO D , INTERDONATO R , et al . SenCLIP: Enhancing Zero-Shot Land-Use Mapping for Sentinel-2 with Ground-Level Prompting [C]// IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , Tucson, AZ, USA, 2025 .
LIU C Y , CHEN K Y , ZHAO R , et al . Text2Earth: Unlocking Text-Driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model [J]. IEEE Geoscience and Remote Sensing Magazine , 2025 , 13 ( 3 ): 238 - 259 .
HE Y G , CHENG X J , ZHU J J , et al . SAR-TEXT: A Large-Scale SAR Image-Text Dataset Built with SAR-Narrator and a Progressive Learning Strategy for Downstream Tasks [PP/OL]. V3. arXiv ( 2025-10-04 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2507.18743 https://doi.org/10.48550/arXiv.2507.18743 .
JIANG C W , WANG C , WU F , et al . SARCLIP: A Multimodal Foundation Framework for SAR Imagery via Contrastive Language-Image Pre-Training [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2026 , 231 : 17 - 34 .
JAIN P , MARCOS D , IENCO D , et al . TimeSenCLIP: A Time Series Vision-Language Model for Remote Sensing [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2026 , 236 : 99 - 119 .
ZHU Q , LAO J W , JI D Y , et al . SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Nashville, TN, USA, 2025 .
LI K Y , LIU R X , CAO X Y , et al . SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Nashville, TN, USA, 2025 .
CAO Q L , CHEN Y T , MA C , et al . Open-Vocabulary High-Resolution Remote Sensing Image Semantic Segmentation [J]. IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 5620614 .
RADFORD A , KIM J W , HALLACY C , et al . Learning Transferable Visual Models from Natural Language Supervision [C]// The 38th International Conference on Machine Learning , Online , 2021 .
ZHONG Y W , YANG J W , ZHANG P C , et al . RegionCLIP: Region-Based Language-Image Pretraining [C]// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , New Orleans, LA, USA, 2022 .
BAZI Y , BASHMAL L , AL RAHHAL M M , et al . RS-LLaVA: A Large Vision-Language Model for Joint Captioning and Question Answering in Remote Sensing Imagery [J]. Remote Sensing , 2024 , 16 ( 9 ): 1477 .
MUHTAR D , LI Z S , GU F , et al . LHRS-Bot: Empowering Remote Sensing With VGI-Enhanced Large Multimodal Language Model [C]// Computer Vision—ECCV 2024 . 2025 .
LUO J W , PANG Z , ZHANG Y J , et al . SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding [PP/OL]. V2. arXiv ( 2024-07-08 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2406.10100 https://doi.org/10.48550/arXiv.2406.10100 .
ZHAN Y , XIONG Z T , YUAN Y . SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2025 , 221 : 64 - 77 .
PANG C , WENG X X , WU J , et al . VHM: Versatile and Honest Vision Language Model for Remote Sensing Image Analysis [J]. Proceedings of the AAAI Conference on Artificial Intelligence , 2025 , 39 ( 6 ): 6381 - 6388 .
LI Z S , MUHTAR D , GU F , et al . LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2025 , 227 : 539 - 550 .
ZHANG W , CAI M X , ZHANG T , et al . EarthMarker: A Visual Prompting Multimodal Large Language Model for Remote Sensing [J]. IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 5604219 .
OU R Z , HU Y , ZHANG F , et al . GeoPix: A Multimodal Large Language Model for Pixel-Level Image Understanding in Remote Sensing [J]. IEEE Geoscience and Remote Sensing Magazine , 2025 , 13 ( 3 ): 324 - 337 .
SHABBIR A , ZUMRI M , BENNAMOUN M , et al . GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing [C]// International Conference on Machine Learning , Vancouver, Canada , 2025 .
LI K Y , XIN Z P , PANG L , et al . SegEarth-R1: Geospatial Pixel Reasoning via Large Language Model [PP/OL]. V1.arXiv ( 2025-04-13 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2504.09644 https://doi.org/10.48550/arXiv.2504.09644 .
XIN Z , LI K , CHEN L , et al . SegEarth-R2: Towards Comprehensive Language-Guided Segmentation for Remote Sensing Images [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition , Nashville, Tennessee,USA,2025 .
NI S , WANG D , CHEN H , et al . UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition , Denver,Colorado, USA,2026 .
AN X , ZHONG C , SUN J X , et al . Beyond Single and Earthbound: Exploring Multi-Image Grounding in Remote Sensing with Large Vision-Language Models [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2026 , 238 : 589 - 605 .
ZHANG W , CAI M X , ZHANG T , et al . EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain [J]. IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5917820 .
ZHANG W , CAI M X , NING Y Q , et al . EarthGPT-X: A Spatial MLLM for Multilevel Multisource Remote Sensing Imagery Understanding With Visual Prompting [J]. IEEE Transactions on Geoscience and Remote Sensing , 2025 , 63 : 4709221 .
SONI S , DUDHANE A , DEBARY H , et al . EarthDial: Turning Multi-Sensory Earth Observations to Interactive Dialogues [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , Nashville, TN, USA, 2025 .
SHU Y , REN B , XIONG Z T , et al . EarthMind: Leveraging Cross-Sensor Data for Advanced Earth Observation Interpretation with a Unified Multimodal LLM [PP/OL]. V2. arXiv ( 2025-09-28 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2506.01667 https://doi.org/10.48550/arXiv.2506.01667 .
CAI M X , WANG G Q , ZHANG W , et al . Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks [PP/OL]. arXiv ( 2026-06-09 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2606.10819 https://doi.org/10.48550/arXiv.2606.10819 .
DENG P , ZHOU W Q , WU H L . ChangeChat: An Interactive Model for Remote Sensing Change Analysis via Multimodal Instruction Tuning [C]// IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Hyderabad, India , 2025 .
IRVIN J A , LIU E R , CHEN J C , et al . TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data [C]// International Conference on Learning Representations (ICLR) , Singapore , 2025 .
XUAN W H , WANG J J , QI H L , et al . DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding [C]// Advances in Neural Information Processing Systems 38, San Diego, California, USA and Mexico City , Mexico , 2025 .
WANG F X , CHEN M S , LI Y Y , et al . GeoLLaVA-8K: Scaling Remote-Sensing Multimodal Large Language Models to 8K Resolution [PP/OL]. V2. arXiv ( 2025-11-04 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2505.21375 https://doi.org/10.48550/arXiv.2505.21375 .
LIU R X , FU B W , SONG J Y , et al . ZoomEarth: Active Perception for Ultra-High-Resolution Geospatial Vision-Language Tasks [PP/OL]. V1.arXiv ( 2025-11-15 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2511.12267 https://doi.org/10.48550/arXiv.2511.12267 .
ZHU J S , FU R H , HU J S , et al . GeoVista: Visually Grounded Active Perception for Vision-Language Understanding of Ultra-High-Resolution Remote Sensing Images [PP/OL]. V2. arXiv ( 2026-06-21 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2605.14475 https://doi.org/10.48550/arXiv.2605.14475 .
FIAZ M , DEBARY H , FRACCARO P , et al . GeoVLM-R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning [PP/OL]. V3. arXiv ( 2025-10-14 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2509.25026 https://doi.org/10.48550/arXiv.2509.25026 .
LIU J , SUN L , FU R , et al . Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models [C]// International Conference on Learning Representations , Sydney, Australia , 2026 .
LI W S , XIANG X T , WEN Z X , et al . GeoReason: Aligning Thinking and Answering in Remote Sensing Vision-Language Models via Logical Consistency Reinforcement Learning [PP/OL]. V2. arXiv ( 2026-01-08 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2601.04118 https://doi.org/10.48550/arXiv.2601.04118 .
LIU H T , LI C Y , WU Q Y , et al . Visual Instruction Tuning [C]// Advances in Neural Information Processing Systems 36 , New Orleans, Louisiana, USA, 2023 .
SEBAQ A , ELHELW M . RSDiff: Remote Sensing Image Generation from Text Using Diffusion Model [J]. Neural Computing and Applications , 2024 , 36 ( 36 ): 23103 - 23111 .
KHANNA S , LIU P , ZHOU L Q , et al . DiffusionSat: A Generative Foundation Model for Satellite Imagery [C]// International Conference on Learning Representations (ICLR) , Vienna,Austria , 2024 .
CHEN Z Y , ZHANG Z C , ZHANG F . RSEdit: Text-Guided Image Editing for Remote Sensing [PP/OL]. V2.arXiv ( 2026-05-18 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2603.13708 https://doi.org/10.48550/arXiv.2603.13708 .
ZHANG W Z , CHEN Z L , ZHANG M X , et al . GeoOne: Unifying Structural and Semantic Control in Diffusion Models via Temporal Decoupling [J]. Knowledge-Based Systems , 2026 , 343 : 116023 .
ZHAO R , SHI Z W . Text-to-Remote-Sensing-Image Generation With Structured Generative Adversarial Networks [J]. IEEE Geoscience and Remote Sensing Letters , 2022 , 19 : 8010005 .
TANG D T , CAO X Y , HOU X S , et al . CRS-Diff: Controllable Remote Sensing Image Generation With Diffusion Model [J]. IEEE Transactions on Geoscience and Remote Sensing , 2024 , 62 : 5638714 .
HOU Y Y , WEI Z H , REN C , et al . UniEarth: A Unified Foundation Model for Remote Sensing Image Generation and Understanding [J]. IEEE Transactions on Geoscience and Remote Sensing , 2026 , 64 : 4707619 .
ZHANG W Y , HU Y , LI Y , et al . Uni-RS: A Spatially Faithful Unified Understanding and Generation Model for Remote Sensing [PP/OL]. V1.arXiv ( 2026-01-25 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2601.17673 https://doi.org/10.48550/arXiv.2601.17673 .
LEWIS A , OLIVER S , LYMBURNER L , et al . The Australian Geoscience Data Cube—Foundations and Lessons Learned [J]. Remote Sensing of Environment , 2017 , 202 : 276 - 292 .
DHU T , DUNN B , LEWIS B , et al . Digital Earth Australia-Unlocking New Value from Earth Observation Data [J]. Big Earth Data , 2017 , 1 ( 1/2 ): 64 - 74 .
DWYER J L , ROY D P , SAUER B , et al . Analysis Ready Data: Enabling Analysis of the Landsat Archive [J]. Remote Sensing , 2018 , 10 ( 9 ): 1363 .
CLAVERIE M , JU J C , MASEK J G , et al . The Harmonized Landsat and Sentinel-2 Surface Reflectance Data Set [J]. Remote Sensing of Environment , 2018 , 219 : 145 - 161 .
JU J C , ZHOU Q , FREITAG B , et al . The Harmonized Landsat and Sentinel-2 Version 2.0 Surface Reflectance Dataset [J]. Remote Sensing of Environment , 2025 , 324 : 114723 .
Open Geospatial Consortium . SpatioTemporal Asset Catalog (STAC) Community Standard 1.1.0: OGC 25-004 [S/OL]. 2025 [2026-10-10]. https://www.ogc.org/standards/stac/ https://www.ogc.org/standards/stac/ .
Open Geospatial Consortium . OGC Cloud Optimized GeoTIFF Standard 1.0: OGC 21-026 [S/OL]. ( 2023-07-14 )[ 2026-10-10 ]. https://docs.ogc.org/is/21-026/21-026.html https://docs.ogc.org/is/21-026/21-026.html .
Zarr Developers . Zarr Core Specification Version 3 [S/OL]. 2023 [2026-10-10]. https://zarr-specs.readthedocs.io/en/latest/v3/core/index.html https://zarr-specs.readthedocs.io/en/latest/v3/core/index.html .
MAHECHA M D , GANS F , BRANDT G , et al . Earth System Data Cubes Unravel Global Multivariate Dynamics [J]. Earth System Dynamics , 2020 , 11 ( 1 ): 201 - 234 .
MONTERO D , AYBAR C , JI C N , et al . On-Demand Earth System Data Cubes [C]// IEEE International Geoscience and Remote Sensing Symposium , Athens, Greece , 2024 .
Microsoft . Planetary Computer Documentation [EB/OL]. [ 2026-10-10 ]. https://planetarycomputer.microsoft.com/docs/changelogs/history https://planetarycomputer.microsoft.com/docs/changelogs/history .
Terrafloww . Rasteret 0.3.5 [EB/OL]. ( 2026-03-13 )[ 2026-10-10 ]. https://github.com/terrafloww/rasteret/releases/tag/v0.3.5 https://github.com/terrafloww/rasteret/releases/tag/v0.3.5 .
GORELICK N , HANCHER M , DIXON M , et al . Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone [J]. Remote Sensing of Environment , 2017 , 202 : 18 - 27 .
龚健雅 , 乐鹏 , 向隆刚 , 等 . 开放地球引擎OGE设计与研制 [J] . 测绘学报 , 2025 , 54 ( 4 ): 587 - 602 .
GONG Jianya , LE Peng , XIANG Longgang , et al . The Design and Implementation of the Open Geospatial Engine(OGE) [J]. Acta Geodaetica et Cartographica Sinica , 2025 , 54 ( 4 ): 587 - 602 .
APPEL M , PEBESMA E . On-Demand Processing of Data Cubes from Satellite Image Collections with the Gdalcubes Library [J]. Data , 2019 , 4 ( 3 ): 92 .
HOYER S , HAMMAN J . Xarray: N-D Labeled Arrays and Datasets in Python [J]. Journal of Open Research Software , 2017 , 5 ( 1 ): 10 .
ABERNATHEY R P , AUGSPURGER T , BANIHIRWE A , et al . Cloud-Native Repositories for Big Scientific Data [J]. Computing in Science & Engineering , 2021 , 23 ( 2 ): 26 - 35 .
ZAYTAR A , ROBINSON C , TADESSE G A , et al . Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data [PP/OL]. V1.arXiv ( 2025-06-06 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2506.06235 https://doi.org/10.48550/arXiv.2506.06235 .
SCHRAMM M , PEBESMA E , MILENKOVIĆ M , et al . The openEO API–Harmonising the Use of Earth Observation Cloud Services Using Virtual Data Cube Functionalities [J]. Remote Sensing , 2021 , 13 ( 6 ): 1125 .
MOHR M , PEBESMA E , DRIES J , et al . Federated and Reusable Processing of Earth Observation Data [J]. Scientific Data , 2025 , 12 : 194 .
Open Geospatial Consortium . OGC API- Processes Part 1: Core 1.0.0: OGC 18-062r2 [S/OL]. ( 2021-12-20 )[ 2026-10-10 ]. https://docs.ogc.org/is/18-062r2/18-062r2.html https://docs.ogc.org/is/18-062r2/18-062r2.html .
Copernicus Data Space Ecosystem . APIs-Documentation [EB/OL]. [ 2026-10-10 ]. https://documentation.dataspace.copernicus.eu/APIs.html https://documentation.dataspace.copernicus.eu/APIs.html .
STEWART A J , ROBINSON C , CORLEY I A , et al . TorchGeo: Deep Learning with Geospatial Data [C]// The 30th International Conference on Advances in Geographic Information Systems , Seattle,WA, USA, 2022 .
GOMES C , BLUMENSTIEL B , DE SOUSA ALMEIDA J L , et al . TerraTorch: The Geospatial Foundation Models Toolkit [C]// IEEE International Geoscience and Remote Sensing Symposium , Brisbane, Australia , 2025 .
Watson C , Wachira C , Lickorish R , et al . TerraStackAI: Bringing Earth and Space AI to Red Hat and the World [EB/OL]. ( 2026-03-03 )[ 2026-10-10 ]. https://research.ibm.com/blog/terrastackai https://research.ibm.com/blog/terrastackai .
SUN C , WANG X Y , LIU Z Q , et al . SiamOHOT: A Lightweight Dual Siamese Network for Onboard Hyperspectral Object Tracking via Joint Spatial-Spectral Knowledge Distillation [J]. IEEE Transactions on Geoscience and Remote Sensing , 2023 , 61 : 5521112 .
GUO J C , WAN Y T , WANG J J , et al . DisasterKD: Frequency-Guided Cross-Decoder Knowledge Distillation for UAV Real-Time Disaster Damage Assessment [J]. Pattern Recognition , 2026 , 174 : 112968 .
IBM Research . AI-Powered Satellites Will Upend How We Observe Our Changing Planet [EB/OL]. ( 2025-10-10 )[ 2026-10-10 ]. https://research.ibm.com/blog/terramind-prithvi-tiny-small-models-geo spatial https://research.ibm.com/blog/terramind-prithvi-tiny-small-models-geospatial .
DEL PRETE R , THIND P K , MAZZEO A , et al . Optimizing Deep Learning Models for On-Orbit Deployment Through Neural Architecture Search [J]. Scientific Reports , 2025 , 15 : 37783 .
GIUFFRIDA G , FANUCCI L , MEONI G , et al . The Φ-Sat-1 Mission: The First On-Board Deep Neural Network Demonstrator for Satellite Earth Observation [J]. IEEE Transactions on Geoscience and Remote Sensing , 2022 , 60 : 5517414 .
MATEO-GARCIA G , VEITCH-MICHAELIS J , SMITH L , et al . Towards Global Flood Mapping Onboard Low Cost Satellites with Machine Learning [J]. Scientific Reports , 2021 , 11 : 7249 .
MATEO-GARCIA G , VEITCH-MICHAELIS J , PURCELL C , et al . In-Orbit Demonstration of a Re-Trainable Machine Learning Payload for Processing Optical Imagery [J]. Scientific Reports , 2023 , 13 : 10391 .
RŮŽIČKA V , VAUGHAN A , DE MARTINI D , et al . RaVÆn: Unsupervised Change Detection of Extreme Events Using ML On-Board Satellites [J]. Scientific Reports , 2022 , 12 : 16939 .
ESA . Φ-sat-2 Begins Science Phase for AI Earth Images [EB/OL]. ( 2025-07-15 )[ 2026-10-10 ]. https://www.esa.int/Applications/Observing_the_Earth/FutureEO/Phsat-2/Phsat-2_begins_science_phase_for_AI_Earth_images https://www.esa.int/Applications/Observing_the_Earth/FutureEO/Phsat-2/Phsat-2_begins_science_phase_for_AI_Earth_images .
LIU S Y , WAN Y T , LI K F , et al . A Brain-Inspired Low-Light and Infrared Multimodal Remote Sensing Detection Network for Nighttime Personnel Search and Rescue [J]. Information Fusion , 2026 , 136 : 104518 .
SINGH S , FORE M , STAMOULIS D . GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots [C]// IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , Seattle, WA, USA, 2024 .
LEE C , PARAMANAYAKAM V , KARATZAS A , et al . Multi-Agent Geospatial Copilots for Remote Sensing Workflows [C]// IEEE International Geoscience and Remote Sensing Symposium , Brisbane, Australia , 2025 .
BELL A , AIDES A , HELMY A , et al . Earth AI: Unlocking Geospatial Insights with Foundation Models and Cross-Modal Reasoning [PP/OL]. V4. arXiv ( 2026-02-18 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2510.18318 https://doi.org/10.48550/arXiv.2510.18318 .
CHEN B E , BÖK T E , RASTI B , et al . REMSA: Foundation Model Selection for Remote Sensing via a Constraint-Aware Agent [PP/OL]. V3. arXiv ( 2026-06-29 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2511.17442 https://doi.org/10.48550/arXiv.2511.17442 .
MUNIR M A , SHEIKH M U , SHABBIR A , et al . Agentic AI for Remote Sensing: Technical Challenges and Research Directions [PP/OL]. V3. arXiv ( 2026-06-01 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2604.24919 https://doi.org/10.48550/arXiv.2604.24919 .
WAN Y T , ZHONG Y F , MA A L , et al . An Accurate UAV 3-D Path Planning Method for Disaster Emergency Response Based on an Improved Multiobjective Swarm Intelligence Algorithm [J]. IEEE Transactions on Cybernetics , 2023 , 53 ( 4 ): 2658 - 2671 .
SONG Y J , OU J W , PEDRYCZ W , et al . Generalized Model and Deep Reinforcement Learning-Based Evolutionary Method for Multitype Satellite Observation Scheduling [J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 2024 , 54 ( 4 ): 2576 - 2589 .
POPOVIĆ M , VIDAL-CALLEJA T , HITZ G , et al . An Informative Path Planning Framework for UAV-Based Terrain Monitoring [J]. Autonomous Robots , 2020 , 44 ( 6 ): 889 - 911 .
WESTHEIDER J , RÜCKIN J , POPOVIĆ M . Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning [C]// IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , Detroit, MI, USA, 2023 .
LI D R , WANG M , DONG Z P , et al . Earth Observation Brain (EOB): An Intelligent Earth Observation System [J]. Geo-spatial Information Science , 2017 , 20 ( 2 ): 134 - 140 .
ZHANG W H , MA S C , JI X L , et al . The Development of General-Purpose Brain-Inspired Computing [J]. Nature Electronics , 2024 , 7 ( 11 ): 954 - 965 .
PEI J , DENG L , SONG S , et al . Towards Artificial General Intelligence with Hybrid Tianjic Chip Architecture [J]. Nature , 2019 , 572 ( 7767 ): 106 - 111 .
YANG Z Y , WANG T Y , LIN Y H , et al . A Vision Chip with Complementary Pathways for Open-World Sensing [J]. Nature , 2024 , 629 ( 8014 ): 1027 - 1033 .
LIU Y H , ZHONG Y F , MA A L , et al . Cross-Resolution National-Scale Land-Cover Mapping Based on Noisy Label Learning: A Case Study of China [J]. International Journal of Applied Earth Observation and Geoinformation , 2023 , 118 : 103265 .
ZHONG Y F , YAN B W , YI J J , et al . Global Urban High-Resolution Land-Use Mapping: From Benchmarks to Multi-Megacity Applications [J]. Remote Sensing of Environment , 2023 , 298 : 113758 .
WANG J J , ZHONG Y F , CHEN Z H , et al . EarthVL: A Progressive Earth Vision-Language Understanding and Generation Framework [PP/OL]. V1.arXiv ( 2026-01-06 )[ 2026-10-10 ]. https://doi.org/10.48550/arXiv.2601.02783 https://doi.org/10.48550/arXiv.2601.02783 .
LIU Y H , ZHONG Y F , SHI S N , et al . Scale-Aware Deep Reinforcement Learning for High Resolution Remote Sensing Imagery Classification [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2024 , 209 : 296 - 311 .
ZHENG Z , ZHONG Y F , WANG J J , et al . Building Damage Assessment for Rapid Disaster Response with a Deep Object-Based Semantic Change Detection Framework: From Natural Disasters to Man-Made Disasters [J]. Remote Sensing of Environment , 2021 , 265 : 112636 .
MUTTENTHALER L , GREFF K , BORN F , et al . Aligning Machine and Human Visual Representations Across Abstraction Levels [J]. Nature , 2025 , 647 ( 8089 ): 349 - 355 .
XU W J , YU Z J , MU B Y , et al . RS-Agent: Automating Remote Sensing Tasks Through Intelligent Agent [J]. Science China Information Sciences , 2026 , 69 ( 8 ): 180302 .
WANG S , HAN W , HUANG X H , et al . Trustworthy Remote Sensing Interpretation: Concepts, Technologies, and Applications [J]. ISPRS Journal of Photogrammetry and Remote Sensing , 2024 , 209 : 150 - 172 .
CHEN Z H , ASADI SHAMSABADI E , JIANG S , et al . Integration of Large Vision Language Models for Efficient Post-Disaster Damage Assessment and Reporting [J]. Nature Communications , 2026 , 17 : 1481 .
0
Views
0
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621