zhou777/LandAI-Base

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

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[Paper (Under Review)]

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📖 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. 1.A robust baseline for geospatial reasoning tasks (Q&A, analysis).
2. 2.The "Cold Start" initialization for the 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 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.

📂 Dataset Composition

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

🛠️ 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.