mlsanityData to 14 Oct 2026

RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning

arXiv 2409.13366 ↗First posted 20 Sep 2024cs.CV cs.AIcited by 15

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Abstract (arXiv)

Aerial Remote Sensing (ARS) vision tasks present significant challenges due to the unique viewing angle characteristics. Existing research has primarily focused on algorithms for specific tasks, which have limited applicability in a broad range of ARS vision applications. This paper proposes RingMo-Aerial, aiming to fill the gap in foundation model research in the field of ARS vision. A Frequency-Enhanced Multi-Head Self-Attention (FE-MSA) mechanism is introduced to strengthen the model's capacity for small-object representation. Complementarily, an affine transformation-based contrastive learning method improves its adaptability to the tilted viewing angles inherent in ARS tasks. Furthermore, the ARS-Adapter, an efficient parameter fine-tuning method, is proposed to improve the model's adaptability and performance in various ARS vision tasks. Experimental results demonstrate that RingMo-Aerial achieves SOTA performance on multiple downstream tasks. This indicates the practicality and efficacy of RingMo-Aerial in enhancing the performance of ARS vision tasks.

Key facts. Cited by 15 papers in the map (data through 14 Oct 2026). Builds on 120 of its 179 references that are in the map. First posted on arXiv 20 Sep 2024.

Builds on

120 of 179 references.

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Citing papers per year: 2025: 5; 2026: 10.

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  1. 2025TESSERA: Temporal Embeddings of Surface Spectra for Earth Representation and Analysiscited by 39
  2. 2025A Survey on Remote Sensing Foundation Models: From Vision to Multimodalitycited by 19
  3. 2026Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodalitycited by 12
  4. 2026Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systemscited by 6
  5. 2025FUSAR-KLIP: Towards Multimodal Foundation Models for Remote Sensingcited by 3
  6. 2025Survey of Multimodal Geospatial Foundation Models: Techniques, Applications, and Challengescited by 3
  7. 2025MAESTRO: Masked AutoEncoders for Multimodal, Multitemporal, and Multispectral Earth Observation Datacited by 2
  8. 2026HighFM: Towards a Foundation Model for Learning Representations from High-Frequency Earth Observation Datacited by 2
  9. 2026SIGMAE: A Spectral-Index-Guided Foundation Model for Multispectral Remote Sensingcited by 1
  10. 2026GAAT: Geometry-Aware Alignment Transformer for Multimodal UAV Perceptioncited by 1
  11. 2026SemDINO: Foundation Prior-Guided Cross-Temporal Semantic Alignment Network for Remote Sensing Change Detectioncited by 0
  12. 2026Training-Free Open-Vocabulary Visual Grounding for Remote Sensing Images and Videoscited by 0
3 more
  1. 2026DAPM: UAV Monocular Depth Estimation from Any Height, Pitch, Roll and FOVcited by 0
  2. 2026Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bankcited by 0
  3. 2026Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agentscited by 0