This paper presents GeoDecoder, a dedicated multimodal model designed for
processing geospatial information in maps. Built on the BeitGPT architecture,
GeoDecoder incorporates specialized expert modules for image and text
processing. On the image side, GeoDecoder utilizes GaoDe Amap as the underlying
base map, which inherently encompasses essential details about road and
building shapes, relative positions, and other attributes. Through the
utilization of rendering techniques, the model seamlessly integrates external
data and features such as symbol markers, drive trajectories, heatmaps, and
user-defined markers, eliminating the need for extra feature engineering. The
text module of GeoDecoder accepts various context texts and question prompts,
generating text outputs in the style of GPT. Furthermore, the GPT-based model
allows for the training and execution of multiple tasks within the same model
in an end-to-end manner. To enhance map cognition and enable GeoDecoder to
acquire knowledge about the distribution of geographic entities in Beijing, we
devised eight fundamental geospatial tasks and conducted pretraining of the
model using large-scale text-image samples. Subsequently, rapid fine-tuning was
performed on three downstream tasks, resulting in significant performance
improvements. The GeoDecoder model demonstrates a comprehensive understanding
of map elements and their associated operations, enabling efficient and
high-quality application of diverse geospatial tasks in different business
scenarios.

GeoDecoder 是一个专门为处理地理空间信息的多模态模型，通过利用 GaoDe Amap 作为底图，该模型具有对道路、建筑形状、相对位置和其他属性的重要细节的隐含理解，同时能够接受各种上下文文本和问题提示，并生成符合 GPT 风格的文本输出，在单个模型中进行多个任务的训练和执行，并能在不同商业场景中高效应用各种地理空间任务。