mlsanityData to 7 Oct 2026

DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation Benchmark

arXiv 2411.02733 ↗First posted 5 Nov 2024cs.CVcited by 5

On the map: Multimodal LLMs / vision-language models → Embodied and 3D vision-language models

Abstract (arXiv)

With the rapid development of large vision language models (LVLMs), these models have shown excellent results in various multimodal tasks. Since LVLMs are prone to hallucinations and there are currently few datasets and evaluation methods specifically designed for remote sensing, their performance is typically poor when applied to remote sensing tasks. To address these issues, this paper introduces a high quality remote sensing LVLMs dataset, DDFAV, created using data augmentation and data mixing strategies. Next, a training instruction set is produced based on some high-quality remote sensing images selected from the proposed dataset. Finally, we develop a remote sensing LVLMs hallucination evaluation method RSPOPE based on the proposed dataset and evaluate the zero-shot capabilities of different LVLMs. Our proposed dataset, instruction set, and evaluation method files are available at https://github.com/HaodongLi2024/rspope.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 22 of its 36 references that are in the map. First posted on arXiv 5 Nov 2024.

Builds on

22 of 36 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensing24 Nov
  2. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensing19 Jun

Other references · 20

  1. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  2. 2023Visual Instruction Tuningcited by 7,792
  3. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  4. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
  5. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  6. 2023Evaluating Object Hallucination in Large Vision-Language Modelscited by 1,696
  7. 2023Shikra: Unleashing Multimodal LLM's Referential Dialogue Magiccited by 891
  8. 2017DOTA: A Large-scale Dataset for Object Detection in Aerial Imagescited by 622
  9. 2023OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocationcited by 468
  10. 2019Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmarkcited by 339
  11. 2020RSVQA: Visual Question Answering for Remote Sensing Datacited by 197
  12. 2017Exploring Models and Data for Remote Sensing Image Caption Generationcited by 195
8 more
  1. 2024Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMscited by 174
  2. 2021FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagerycited by 143
  3. 2024Detecting and Mitigating Hallucination in Large Vision Language Models via Fine-Grained AI Feedbackcited by 80
  4. 2024Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attentioncited by 74
  5. 2023EarthVQA: Towards Queryable Earth via Relational Reasoning-Based Remote Sensing Visual Question Answeringcited by 51
  6. 2023SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Modelcited by 38
  7. 2024CODE: Contrasting Self-generated Description to Combat Hallucination in Large Multi-modal Modelscited by 37
  8. 2024Alleviating Hallucination in Large Vision-Language Models with Active Retrieval Augmentationcited by 27

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Med-CMR: A Fine-Grained Benchmark Integrating Visual Evidence and Clinical Logic for Medical Complex Multimodal Reasoningcited by 14
  2. 2025Asking like Socrates: Socrates helps VLMs understand remote sensing imagescited by 6
  3. 2026GRASP: Guided Region-Aware Sparse Prompting for Adapting MLLMs to Remote Sensingcited by 5
  4. 2026GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysiscited by 2
  5. 2026DARAD: Dual Adapters and Ranking-Aware Distillation for Continual Remote Sensing Image-Text Retrievalcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
  2. 2023RSGPT: A Remote Sensing Vision Language Model and Benchmark
  3. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives
  4. 2025XLRS-Bench: Could Your Multimodal LLMs Understand Extremely Large Ultra-High-Resolution Remote Sensing Imagery?
  5. 2026Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?
  6. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing
  7. 2025Measuring and Mitigating Hallucinations in Vision-Language Dataset Generation for Remote Sensing
  8. 2025Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation