mlsanityData to 7 Oct 2026

REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation

arXiv 2412.16583 ↗First posted 21 Dec 2024cs.CVcited by 9

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

Abstract (arXiv)

The rapid evolution of Vision Language Models (VLMs) has catalyzed significant advancements in artificial intelligence, expanding research across various disciplines, including Earth Observation (EO). While VLMs have enhanced image understanding and data processing within EO, their applications have predominantly focused on image content description. This limited focus overlooks their potential in geographic and scientific regression tasks, which are essential for diverse EO applications. To bridge this gap, this paper introduces a novel benchmark dataset, called REO-Instruct to unify regression and generation tasks specifically for the EO domain. Comprising 1.6 million multimodal EO imagery and language pairs, this dataset is designed to support both biomass regression and image content interpretation tasks. Leveraging this dataset, we develop REO-VLM, a groundbreaking model that seamlessly integrates regression capabilities with traditional generative functions. By utilizing language-driven reasoning to incorporate scientific domain knowledge, REO-VLM goes beyond solely relying on EO imagery, enabling comprehensive interpretation of complex scientific attributes from EO data. This approach establishes new performance benchmarks and significantly enhances the capabilities of environmental monitoring and resource management.

Key facts. Cited by 9 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 25 of its 35 references that are in the map. First posted on arXiv 21 Dec 2024.

Builds on

25 of 35 references.

Closest ancestors · 11

Same direction, newest first

  1. 2024SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding14 Jun
  2. 2024Towards Vision-Language Geo-Foundation Model: A Survey13 Jun
  3. 2024LHRS-Bot: Empowering Remote Sensing with VGI-Enhanced Large Multimodal Language Model4 Feb
  4. 2024EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain30 Jan
  5. 2024SkyEyeGPT: Unifying Remote Sensing Vision-Language Tasks via Instruction Tuning with Large Language Model18 Jan
  6. 2023SkyScript: A Large and Semantically Diverse Vision-Language Dataset for Remote Sensing20 Dec
  7. 2023Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment12 Dec
  8. 2023GeoChat: Grounded Large Vision-Language Model for Remote Sensing24 Nov
  9. 2023RSGPT: A Remote Sensing Vision Language Model and Benchmark28 Jul
  10. 2023RS5M and GeoRSCLIP: A Large Scale Vision-Language Dataset and A Large Vision-Language Model for Remote Sensing20 Jun
  11. 2023RemoteCLIP: A Vision Language Foundation Model for Remote Sensing19 Jun

Other references · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2023Visual Instruction Tuningcited by 7,792
  5. 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolutioncited by 4,151
  6. 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
  7. 2022A high-resolution canopy height model of the Earthcited by 91
  8. 2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modelingcited by 46
  9. 2024Large Language Models for Captioning and Retrieving Remote Sensing Imagescited by 30
  10. 2024TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learningcited by 29
  11. 2023Multi-modal learning for geospatial vegetation forecastingcited by 16
  12. 2024AGBD: A Global-scale Biomass Datasetcited by 13
2 more
  1. 2024Combining Observational Data and Language for Species Range Estimationcited by 12
  2. 2024M3LEO: A Multi-Modal, Multi-Label Earth Observation Dataset Integrating Interferometric SAR and Multispectral Datacited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 6.

9 papers.

  1. 2025When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruningcited by 35
  2. 2025Object Detection with Multimodal Large Vision-Language Models: An In-depth Reviewcited by 14
  3. 2026Foundation Models in Remote Sensing: Evolving from Unimodality to Multimodalitycited by 12
  4. 2025LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectivescited by 4
  5. 2026OSMDA: OpenStreetMap-based Domain Adaptation for Remote Sensing VLMscited by 2
  6. 2026RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observationcited by 2
  7. 2026AirSpatialBot: A Spatially-Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognization and Retrievalcited by 1
  8. 2026Robust Onion: Peeling Open Vocab Object Detectors Under Noisecited by 1
  9. 2026Do Foundation Models Know Geometry? Probing Frozen Features for Continuous Physical Measurementcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Regression in EO: Are VLMs Up to the Challenge?
  2. 2025Towards Unified Vision Language Models for Forest Ecological Analysis in Earth Observation
  3. 2025Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives
  4. 2024Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
  5. 2026OmniEarth: A Benchmark for Evaluating Vision-Language Models in Geospatial Tasks
  6. 2026More with Less: a Large Scale Remote Sensing VLM with a Simple Recipe
  7. 2024CHOICE: Benchmarking the Remote Sensing Capabilities of Large Vision-Language Models
  8. 2024LHRS-Bot-Nova: Improved Multimodal Large Language Model for Remote Sensing Vision-Language Interpretation