mlsanityData to 7 Oct 2026

VLRS-Bench: A Vision-Language Reasoning Benchmark for Remote Sensing

arXiv 2602.07045 ↗First posted 4 Feb 2026cs.CV cs.AIcited by 6

On the map: LLM reasoning → Spatial and embodied reasoning

also in 1 more
Abstract (arXiv)

Recent advancements in Multimodal Large Language Models (MLLMs) have enabled complex reasoning. However, existing remote sensing (RS) benchmarks remain heavily biased toward perception tasks, such as object recognition and scene classification. This limitation hinders the development of MLLMs for cognitively demanding RS applications. To address this, we propose a Vision Language ReaSoning Benchmark (VLRS-Bench), which is the first benchmark exclusively dedicated to complex RS reasoning. Structured across the three core dimensions of Cognition, Decision, and Prediction, VLRS-Bench comprises 2,000 question-answer pairs with an average question length of 130.19 words, spanning 14 tasks and up to eight temporal phases. VLRS-Bench is constructed via a specialized pipeline that integrates RS-specific priors and expert knowledge to ensure geospatial realism and reasoning complexity. Experimental results reveal significant bottlenecks in existing state-of-the-art MLLMs, providing critical insights for advancing multimodal reasoning within the remote sensing community. The project repository is available at https://github.com/MiliLab/VLRS-Bench.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning. Builds on 48 of its 78 references that are in the map. First posted on arXiv 4 Feb 2026.

Builds on

48 of 78 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models27 Nov

Other references · 47

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35 more
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  5. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensingcited by 320
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  13. 2024LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Modelcited by 133
  14. 2022RSVG: Exploring Data and Models for Visual Grounding on Remote Sensing Datacited by 121
  15. 2018Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Modelscited by 118
  16. 2024VRSBench: A Versatile Vision-Language Benchmark Dataset for Remote Sensing Image Understandingcited by 116
  17. 2024SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understandingcited by 101
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

  1. 2026RSVideo: Are Your Vision-Language Models Ready for Remote Sensing Videos?cited by 1
  2. 2026Hard Vision, Easy Vision: What GPT-6 Astra Reveals Across Computer Visioncited by 1
  3. 2026More with Less: a Large Scale Remote Sensing VLM with a Simple Recipecited by 0
  4. 2026RS-RIE-Bench: Benchmarking Reasoning-Guided Remote Sensing Image Editingcited by 0
  5. 2026RRS-10K: A Multitask Vision-Language Model Benchmark for Rare Remote Sensing Image Interpretationcited by 0
  6. 2026LongEarth-R1: Benchmarking and Aligning Vision-Language Models for Long-Horizon Earth Observation Reasoningcited by 0

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  3. 2026OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks
  4. 2026Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?
  5. 2025GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing
  6. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives
  7. 2026EarthSpatialBench: Benchmarking Spatial Reasoning Capabilities of Multimodal LLMs on Earth Imagery
  8. 2025TinyRS-R1: Compact Multimodal Language Model for Remote Sensing