Aerial Image (Roads & Sidewalks/ Crosswalks)

In this study called "GeoSAM", we leverage high-resolution orthorectified imagery and GIS data to train and evaluate the GeoSAM model, aimed at improving the segmentation of mobility infrastructure in urban environments. Orthorectified images, provided by the U.S. Geological Survey, serve as a precise and corrected form of aerial imagery, which, along with GIS data, are used to create accurate annotation labels. Our methodology involves using these datasets across two distinct regions within Washington DC for model training and evaluation. Additionally, to assess the model's generalization capabilities, we introduce image tiles from Cambridge—a city not seen during training—thereby testing GeoSAM's ability to adapt and perform in new, unseen environments. The images prepared for GeoSAM are of (1024x1024) resolution, with a zoom level of 20, assembled from adjacent tiles to meet the required dimensions. This approach underscores the robustness and wide applicability of GeoSAM across various geographical locations, demonstrating its potential in enhancing the accuracy of urban infrastructure mapping through advanced image processing techniques.