---
license: apache-2.0
language:
- en
- zh
pipeline_tag: image-text-to-text
tags:
- remote-sensing
- geospatial-reasoning
- qwen2.5-vl
- sft
- chain-of-thought
- ms-swift
---

# LandAI-Base: Activating Geospatial Chain-of-Thought Reasoning

<div align="center">

**[Paper (Under Review)]** 

</div>

## 📖 Introduction

**LandAI-Base** is the foundational **Supervised Fine-Tuned (SFT)** model of the LandAI family. Built upon the **Qwen2.5-VL-7B-Instruct** architecture, it is specifically designed to activate domain-specific logical reasoning in Earth Observation tasks.

Unlike general-purpose multimodal models, LandAI-Base has been fine-tuned on a composite corpus of approximately **334,000 reasoning chains**, including the novel **Geo-Base-Thinking-14K** dataset. This process instills the model with the "epistemological authority" of geography experts, enabling it to decompose complex spatial problems before engaging in visual recognition.

**LandAI-Base serves two primary purposes:**
1.  **A robust baseline** for geospatial reasoning tasks (Q&A, analysis).
2.  **The "Cold Start" initialization** for the [LandAI-L1](https://huggingface.co/zhou777/LandAI-L1) model (trained via GRPO-L1).

## 🚀 Key Features

* **Domain-Specific Cognitive Activation**: Fine-tuned to simulate the reasoning patterns of geography experts, moving from rote memorization to logical deduction.
* **High-Quality Training Data**: Trained on a curated mix of:
    * **Geo-Base-Thinking-14K**: ~14.7k distillations from geography entrance exams and textbooks.
    * **General Reasoning Corpus**: Subsets from OpenR1-Math, OpenThoughts, and Chinese-Data-R1 to enhance mathematical and scientific logic.
* **Strong Zero-Shot Performance**: Significantly outperforms the vanilla Qwen2.5-VL-7B on geographic benchmark exams.
* **MS-Swift Compatibility**: Fully compatible with the [ms-swift](https://github.com/modelscope/swift) training framework.

## 📊 Performance Benchmarks

LandAI-Base demonstrates a substantial leap in reasoning capabilities compared to its backbone model. In the **GeoTest2025** benchmark (derived from restricted 2025 National Postgraduate Entrance Examination questions), it achieves near-commercial performance.

| Model | GeoTest2025 (Geography) | AIME 2024  | HumanEval | MMMU pro |
| :--- | :---: | :---: | :---: | :---: |
| **LandAI-Base-7B (Ours)** | **93.3%** | **16.7%** | **66.4%** | **44.7%** |
| Qwen2.5-VL-7B (Baseline) | 46.7% | 3.3% | 67.3% | 41.2% |
| GPT-4o | 92.1% | 9.3% | 90.2% | 51.9% |
| Gemini 2.5 Pro | 98.3% | 92.0% | - | 71.2% |



## 📂 Dataset Composition

The explicit reasoning capability of LandAI-Base stems from its training data distribution:

| Dataset Source | Samples | Purpose |
| :--- | :--- | :--- |
| **Geo-Base-Thinking-14K** | ~14.7k | Domain-specific geospatial logic & knowledge |
| **OpenR1-Math** | ~96k | Mathematical reasoning infrastructure |
| **OpenThoughts** | ~114k | General scientific literacy (Physics/Chem/Bio) |
| **Chinese-Data-R1** | ~110k | Linguistic nuance and logic bridging |

## 🛠️ Quick Start

LandAI-Base follows the standard **Qwen2.5-VL** architecture. You can use it for geospatial Question Answering or as a base for further RL training.