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--- |
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license: apache-2.0 |
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language: |
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- en |
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- zh |
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pipeline_tag: image-text-to-text |
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tags: |
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- remote-sensing |
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- geospatial-reasoning |
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- qwen2.5-vl |
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- sft |
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- chain-of-thought |
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- ms-swift |
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--- |
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# LandAI-Base: Activating Geospatial Chain-of-Thought Reasoning |
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<div align="center"> |
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**[Paper (Under Review)]** |
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</div> |
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## 📖 Introduction |
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**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. |
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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. |
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**LandAI-Base serves two primary purposes:** |
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1. **A robust baseline** for geospatial reasoning tasks (Q&A, analysis). |
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2. **The "Cold Start" initialization** for the [LandAI-L1](https://huggingface.co/zhou777/LandAI-L1) model (trained via GRPO-L1). |
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## 🚀 Key Features |
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* **Domain-Specific Cognitive Activation**: Fine-tuned to simulate the reasoning patterns of geography experts, moving from rote memorization to logical deduction. |
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* **High-Quality Training Data**: Trained on a curated mix of: |
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* **Geo-Base-Thinking-14K**: ~14.7k distillations from geography entrance exams and textbooks. |
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* **General Reasoning Corpus**: Subsets from OpenR1-Math, OpenThoughts, and Chinese-Data-R1 to enhance mathematical and scientific logic. |
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* **Strong Zero-Shot Performance**: Significantly outperforms the vanilla Qwen2.5-VL-7B on geographic benchmark exams. |
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* **MS-Swift Compatibility**: Fully compatible with the [ms-swift](https://github.com/modelscope/swift) training framework. |
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## 📊 Performance Benchmarks |
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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. |
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| Model | GeoTest2025 (Geography) | AIME 2024 | HumanEval | MMMU pro | |
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| :--- | :---: | :---: | :---: | :---: | |
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| **LandAI-Base-7B (Ours)** | **93.3%** | **16.7%** | **66.4%** | **44.7%** | |
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| Qwen2.5-VL-7B (Baseline) | 46.7% | 3.3% | 67.3% | 41.2% | |
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| GPT-4o | 92.1% | 9.3% | 90.2% | 51.9% | |
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| Gemini 2.5 Pro | 98.3% | 92.0% | - | 71.2% | |
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## 📂 Dataset Composition |
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The explicit reasoning capability of LandAI-Base stems from its training data distribution: |
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| Dataset Source | Samples | Purpose | |
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| :--- | :--- | :--- | |
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| **Geo-Base-Thinking-14K** | ~14.7k | Domain-specific geospatial logic & knowledge | |
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| **OpenR1-Math** | ~96k | Mathematical reasoning infrastructure | |
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| **OpenThoughts** | ~114k | General scientific literacy (Physics/Chem/Bio) | |
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| **Chinese-Data-R1** | ~110k | Linguistic nuance and logic bridging | |
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## 🛠️ Quick Start |
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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. |
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