SpotSFT-200k: Visual QA Dataset for Geo-localization Alignment

Project Page

Dataset Description

SpotSFT-200k is a large-scale multimodal instruction-tuning dataset comprising approximately 200,000 image-text pairs. It is designed for the Supervised Fine-Tuning (SFT) stage of the SpotAgent framework (Stage 1).

Unlike the subsequent SpotAgenticCoT dataset which focuses on complex tool use and reasoning, SpotSFT-200k aims to:

1. Inject Basic World Knowledge: Align the Large Vision-Language Model (LVLM) with broad geographical concepts using a massive amount of real-world imagery.
2. Format Alignment: Teach the model to adhere to the specific Geo-localization output format (Country, City, Latitude, Longitude) required for downstream tasks.

Key Features

• Large-Scale Coverage: Contains ~200k samples randomly sampled (5%) from the MP16-Pro dataset, ensuring a diverse distribution of global locations (natural landscapes, urban settings, landmarks).
• High-Quality Metadata: Derived from MP16-Pro, which filters out samples with ambiguous or incomplete metadata while retaining hierarchical textual descriptions (Continent, Country, Region, City).
• Visual QA Format: Formatted as standard multimodal conversation data, transforming raw image-coordinate pairs into an instruction-following task.

Dataset Structure

The dataset follows a standard conversation format compatible with models like Qwen-VL or LLaVA.

Data Fields

• id: Unique identifier for the sample.
• image: The input query image.
• conversations: A list of messages between "user" and "assistant".
  • User: Contains the prompt asking for geo-localization.
  • Assistant: The ground-truth location formatted as a structured answer.

Example Sample

Note: This stage focuses on direct answers or simple reasoning to establish the output format.

{
  "id": "sample_12345",
  "image": "<image_object>",
  "conversations": [
    {
      "from": "user",
      "value": "You are a helpful assistant. Your task is to determine the geographic location of an image through systematic visual analysis.\n<image>\nProvide the final answer inside <answer> ... </answer>."
    },
    {
      "from": "assistant",
      "value": "<answer> Country: France, City: Paris, Latitude: 48.8566, Longitude: 2.3522 </answer>"
    }
  ]
}