# Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Models

arXiv: https://arxiv.org/abs/2401.09083 · HTML: https://mlsanity.com/paper/2401.09083/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://mlsanity.com/paper/2303.04671/) (2023)
- [Remote Sensing Object Detection Meets Deep Learning: A Meta-review of Challenges and Advances](https://mlsanity.com/paper/2309.06751/) (2023)
- [The Potential of Visual ChatGPT For Remote Sensing](https://mlsanity.com/paper/2304.13009/) (2023)
- [Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing](https://mlsanity.com/paper/2309.06047/) (2023)

## Built on by (most cited first, 30 of 32)

- [RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent](https://mlsanity.com/paper/2406.07089/) (2024)
- [Towards Vision-Language Geo-Foundation Model: A Survey](https://mlsanity.com/paper/2406.09385/) (2024)
- [Falcon: A Remote Sensing Vision-Language Foundation Model (Technical Report)](https://mlsanity.com/paper/2503.11070/) (2025)
- [Multi-Agent Geospatial Copilots for Remote Sensing Workflows](https://mlsanity.com/paper/2501.16254/) (2025)
- [An LLM Agent for Automatic Geospatial Data Analysis](https://mlsanity.com/paper/2410.18792/) (2024)
- [A Survey on Remote Sensing Foundation Models: From Vision to Multimodality](https://mlsanity.com/paper/2503.22081/) (2025)
- [OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents](https://mlsanity.com/paper/2602.17665/) (2026)
- [LongFly: Long-Horizon UAV Vision-and-Language Navigation with Spatiotemporal Context Integration](https://mlsanity.com/paper/2512.22010/) (2025)
- [OpenEarth-Agent: From Tool Calling to Tool Creation for Open-Environment Earth Observation](https://mlsanity.com/paper/2603.22148/) (2026)
- [RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language Models](https://mlsanity.com/paper/2408.14744/) (2024)
- [Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems](https://mlsanity.com/paper/2601.01891/) (2026)
- [SAR Strikes Back: A New Hope for RSVQA](https://mlsanity.com/paper/2501.08131/) (2025)
- [Homogeneous Tokenizer Matters: Homogeneous Visual Tokenizer for Remote Sensing Image Understanding](https://mlsanity.com/paper/2403.18593/) (2024)
- [Checkmate: interpretable and explainable RSVQA is the endgame](https://mlsanity.com/paper/2508.13086/) (2025)
- [LLM Agent Framework for Intelligent Change Analysis in Urban Environment using Remote Sensing Imagery](https://mlsanity.com/paper/2601.02757/) (2026)
- [RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation](https://mlsanity.com/paper/2607.24772/) (2026)
- [GPT-4V Explorations: Mining Autonomous Driving](https://mlsanity.com/paper/2406.16817/) (2024)
- [A comprehensive GeoAI review: Progress, Challenges and Outlooks](https://mlsanity.com/paper/2412.11643/) (2024)
- [A Zero-Shot Learning Approach for Ephemeral Gully Detection from Remote Sensing using Vision Language Models](https://mlsanity.com/paper/2503.01169/) (2025)
- [MGCR-Net:Multimodal Graph-Conditioned Vision-Language Reconstruction Network for Remote Sensing Change Detection](https://mlsanity.com/paper/2508.01555/) (2025)
- [Remote Sensing Image Intelligent Interpretation with the Language-Centered Perspective: Principles, Methods and Challenges](https://mlsanity.com/paper/2508.06832/) (2025)
- [MMUEChange: A Generalized LLM Agent Framework for Intelligent Multi-Modal Urban Environment Change Analysis](https://mlsanity.com/paper/2601.05483/) (2026)
- [HiRS-Agent: A Hierarchical Multi-Agent System for Reliable Long-Horizon Remote Sensing Task Solving](https://mlsanity.com/paper/2608.30672/) (2026)
- [Vision-Language Agents for Interactive Forest Change Analysis](https://mlsanity.com/paper/2601.04497/) (2026)
- [Forest-Chat: Adapting Vision-Language Agents for Interactive Forest Change Analysis](https://mlsanity.com/paper/2601.14637/) (2026)
- [Lightweight Multimodal Adaptation of Vision Language Models for Species Recognition and Habitat Context Interpretation in Drone Thermal Imagery](https://mlsanity.com/paper/2604.06124/) (2026)
- [ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding](https://mlsanity.com/paper/2604.22333/) (2026)
- [A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation](https://mlsanity.com/paper/2606.28896/) (2026)
- [Earth-Agent-Pro: Towards Real-World Full-Chain Earth Observation with Agents](https://mlsanity.com/paper/2609.12533/) (2026)
- [GeoSkill:Experience-Driven Hierarchical Skill Learning with Collaborative Revision forGeospatialAgents](https://mlsanity.com/paper/2609.13667/) (2026)

## Abstract (arXiv)

Recently, the flourishing large language models(LLM), especially ChatGPT, have shown exceptional performance in language understanding, reasoning, and interaction, attracting users and researchers from multiple fields and domains. Although LLMs have shown great capacity to perform human-like task accomplishment in natural language and natural image, their potential in handling remote sensing interpretation tasks has not yet been fully explored. Moreover, the lack of automation in remote sensing task planning hinders the accessibility of remote sensing interpretation techniques, especially to non-remote sensing experts from multiple research fields. To this end, we present Remote Sensing ChatGPT, an LLM-powered agent that utilizes ChatGPT to connect various AI-based remote sensing models to solve complicated interpretation tasks. More specifically, given a user request and a remote sensing image, we utilized ChatGPT to understand user requests, perform task planning according to the tasks' functions, execute each subtask iteratively, and generate the final response according to the output of each subtask. Considering that LLM is trained with natural language and is not capable of directly perceiving visual concepts as contained in remote sensing images, we designed visual cues that inject visual information into ChatGPT. With Remote Sensing ChatGPT, users can simply send a remote sensing image with the corresponding request, and get the interpretation results as well as language feedback from Remote Sensing ChatGPT. Experiments and examples show that Remote Sensing ChatGPT can tackle a wide range of remote sensing tasks and can be extended to more tasks with more sophisticated models such as the remote sensing foundation model. The code and demo of Remote Sensing ChatGPT is publicly available at https://github.com/HaonanGuo/Remote-Sensing-ChatGPT .