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A Vision Centric Remote Sensing Benchmark

Abduljaleel Adejumo, Faegheh Yeganli, Clifford Broni-bediako, Aoran Xiao, Naoto Yokoya, Mennatullah Siam

TL;DR

The paper addresses the gap in effective vision-language understanding for remote sensing (RS) imagery, where fine-grained spatial structures and multiple modalities challenge CLIP-based encoders. It proposes RSMMVP, a benchmark built on CLIP-blind pairs and a remote-sensing visual question answering task to probe visual grounding and spatial reasoning, leveraging CLIP-ViT-L/14 and DINOv2 embeddings to identify challenging pairs. Through a 300-question VQA dataset derived from 95 image pairs and evaluated with GPT-4o, the study shows substantial gaps between state-of-the-art MLLMs and human performance, highlighting persistent limitations in RS-specific representation learning. The results underscore the need for RS-tailored vision-language models and provide a concrete benchmark to drive future research in robust, high-resolution geospatial multimodal understanding.

Abstract

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored. Unlike natural images, RS imagery presents unique challenges that current MLLMs struggle to handle, particularly in visual grounding and spatial reasoning. This study investigates the limitations of CLIP-based MLLMs in RS, highlighting their failure to differentiate visually distinct yet semantically similar RS images. To address this, we introduce a remote sensing multimodal visual patterns (RSMMVP) benchmark. It is designed to evaluate MLLMs in RS tasks by identifying the CLIP-blind pairs, where CLIP-based models incorrectly assign high similarity scores to visually distinct RS images. Through a visual question answering (VQA) evaluation, we analyze the performance of state-of-the-art MLLMs, revealing significant limitations in RS specific representation learning. The results provide valuable insights into the weaknesses of CLIP-based visual encoding and offer a foundation for future research to develop more effective MLLMs tailored for remote sensing applications.

A Vision Centric Remote Sensing Benchmark

TL;DR

The paper addresses the gap in effective vision-language understanding for remote sensing (RS) imagery, where fine-grained spatial structures and multiple modalities challenge CLIP-based encoders. It proposes RSMMVP, a benchmark built on CLIP-blind pairs and a remote-sensing visual question answering task to probe visual grounding and spatial reasoning, leveraging CLIP-ViT-L/14 and DINOv2 embeddings to identify challenging pairs. Through a 300-question VQA dataset derived from 95 image pairs and evaluated with GPT-4o, the study shows substantial gaps between state-of-the-art MLLMs and human performance, highlighting persistent limitations in RS-specific representation learning. The results underscore the need for RS-tailored vision-language models and provide a concrete benchmark to drive future research in robust, high-resolution geospatial multimodal understanding.

Abstract

Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored. Unlike natural images, RS imagery presents unique challenges that current MLLMs struggle to handle, particularly in visual grounding and spatial reasoning. This study investigates the limitations of CLIP-based MLLMs in RS, highlighting their failure to differentiate visually distinct yet semantically similar RS images. To address this, we introduce a remote sensing multimodal visual patterns (RSMMVP) benchmark. It is designed to evaluate MLLMs in RS tasks by identifying the CLIP-blind pairs, where CLIP-based models incorrectly assign high similarity scores to visually distinct RS images. Through a visual question answering (VQA) evaluation, we analyze the performance of state-of-the-art MLLMs, revealing significant limitations in RS specific representation learning. The results provide valuable insights into the weaknesses of CLIP-based visual encoding and offer a foundation for future research to develop more effective MLLMs tailored for remote sensing applications.

Paper Structure

This paper contains 5 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Benchmark
  4. Experiments
  5. Conclusion

Figures (5)

  • Figure 1: Examples from our RSMMVP benchmark demonstrating MLLMs shortcomings in RS VQA tasks due to weaknesses in visual grounding and reasoning. Incorrect and correct answers are highlighted in red and green respectively. LLaVA 1.5 7B variant is used in these results.
  • Figure 2: Constructing a benchmark for remote sensing imagery using CLIP-blind pairs. From left to right: (1) CLIP-blind pairs identification (2) Manual construction of VQA task (3) Query MLLMs with CLIP-blind pairs and evaluate responses. A model is scored only if both answers for a CLIP-blind pair are correct.
  • Figure 3: Examples of questions from the RSMMVP benchmark. Incorrect answers are in red. A model is considered correct only if it answers both questions in a pair correctly. LLaVA 1.5 7B variant is used in these results.
  • Figure 4: RSMMVP benchmark results of the current MLLMs with respect to the human performance.
  • Figure 5: Fine-grained performance of current CLIP-based MLLMs on visual patterns in the RSMMVP benchmark. The evaluated visual pattern categories include Object Counting and Quantity Recognition (OCQR), Object Detection and Classification (ODC), Geometric and Spatial Relationships (GSR), Orientation and Directionality (OD), Color Recognition and Classification (CRC), and Size and Proximity Comparisons (SPC).