# Vision-Language Agents for Interactive Forest Change Analysis

arXiv: https://arxiv.org/abs/2601.04497 · HTML: https://mlsanity.com/paper/2601.04497/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 14 of 14)

- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Vision-Language Models in Remote Sensing: Current Progress and Future Trends](https://mlsanity.com/paper/2305.05726/) (2023)
- [TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data](https://mlsanity.com/paper/2410.06234/) (2024)
- [Change-Agent: Towards Interactive Comprehensive Remote Sensing Change Interpretation and Analysis](https://mlsanity.com/paper/2403.19646/) (2024)
- [Vision Foundation Models in Remote Sensing: A Survey](https://mlsanity.com/paper/2408.03464/) (2024)
- [RS-Agent: Automating Remote Sensing Tasks through Intelligent Agent](https://mlsanity.com/paper/2406.07089/) (2024)
- [Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data](https://mlsanity.com/paper/2401.17600/) (2024)
- [Remote Sensing ChatGPT: Solving Remote Sensing Tasks with ChatGPT and Visual Models](https://mlsanity.com/paper/2401.09083/) (2024)
- [CLIP4IDC: CLIP for Image Difference Captioning](https://mlsanity.com/paper/2206.00629/) (2022)
- [ChangeChat: An Interactive Model for Remote Sensing Change Analysis via Multimodal Instruction Tuning](https://mlsanity.com/paper/2409.08582/) (2024)
- [Remote Sensing SpatioTemporal Vision-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02573/) (2024)
- [Tree-GPT: Modular Large Language Model Expert System for Forest Remote Sensing Image Understanding and Interactive Analysis](https://mlsanity.com/paper/2310.04698/) (2023)
- [AI applications in forest monitoring need remote sensing benchmark datasets](https://mlsanity.com/paper/2212.09937/) (2022)

## Abstract (arXiv)

Modern forest monitoring workflows increasingly benefit from the growing availability of high-resolution satellite imagery and advances in deep learning. Two persistent challenges in this context are accurate pixel-level change detection and meaningful semantic change captioning for complex forest dynamics. While large language models (LLMs) are being adapted for interactive data exploration, their integration with vision-language models (VLMs) for remote sensing image change interpretation (RSICI) remains underexplored. To address this gap, we introduce an LLM-driven agent for integrated forest change analysis that supports natural language querying across multiple RSICI tasks. The proposed system builds upon a multi-level change interpretation (MCI) vision-language backbone with LLM-based orchestration. To facilitate adaptation and evaluation in forest environments, we further introduce the Forest-Change dataset, which comprises bi-temporal satellite imagery, pixel-level change masks, and multi-granularity semantic change captions generated using a combination of human annotation and rule-based methods. Experimental results show that the proposed system achieves mIoU and BLEU-4 scores of 67.10% and 40.17% on the Forest-Change dataset, and 88.13% and 34.41% on LEVIR-MCI-Trees, a tree-focused subset of LEVIR-MCI benchmark for joint change detection and captioning. These results highlight the potential of interactive, LLM-driven RSICI systems to improve accessibility, interpretability, and efficiency of forest change analysis. All data and code are publicly available at https://github.com/JamesBrockUoB/ForestChat.