  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 26%, 7%, and 17% 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. The source code can be found on this GitHub repository:
https://github.com/rafiibnsultan/GeoSAM/tree/main.
