The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation. However, it struggles with geographical images like aerial and satellite imagery, especially when segmenting mobility infrastructure including roads, sidewalks, and crosswalks. This inferior performance stems from the narrow features of these objects, their textures blending into the surroundings, and interference from objects like trees, buildings, vehicles, and pedestrians - all of which can disorient the model to produce inaccurate segmentation maps. To address these challenges, we propose Geographical SAM (GeoSAM), a novel SAM-based framework that implements a fine-tuning strategy using the dense visual prompt from zero-shot learning, and the sparse visual prompt from a pre-trained CNN segmentation model. The proposed GeoSAM outperforms existing approaches for geographical image segmentation, specifically by 20%, 14.29%, and 17.65% for road infrastructure, pedestrian infrastructure, and on average, respectively, representing a momentous leap in leveraging foundation models to segment mobility infrastructure including both road and pedestrian infrastructure in geographical images.

提出了一种基于Segment Anything Model (SAM) 的新框架Geographical SAM (GeoSAM)，通过使用来自零样本学习的稠密视觉提示和预训练的CNN分割模型的稀疏视觉提示，实现了对地理图像中的移动设施进行精细调优的策略。所提出的GeoSAM 在地理图像分割中优于现有方法，特别是在道路基础设施、行人基础设施和平均方面分别提高了20%、14.29%和17.65%，在实现对地理图像中包括道路和行人基础设施的移动设施的分割上取得了重大突破。

GeoSAM：稀疏与密集视觉提示的自动化移动基础设施分割中的SAM微调