Use Segment Anything Model (SAM) for Geospatial Data
SAM for Geospatial Data
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Segment Anything Model (SAM) on Apple Silicon M1 and M2
Create object masks from input prompts with SAM
The Segment Anything Model (SAM) is an image segmentation model developed by OpenAI that is capable of cutting out almost anything from an image. While the model was originally developed for general image segmentation, it has shown great potential for use in geospatial data analysis.
Geospatial data is any data that has a geographic component, such as satellite imagery, maps, and aerial photography. The ability to accurately segment this type of data is critical for a range of applications, from disaster response and environmental monitoring to urban planning and agriculture.
Traditionally, creating an accurate segmentation model for geospatial data has required highly specialized work by technical experts with access to AI training infrastructure and large volumes of carefully annotated in-domain data. However, the Segment Anything Model can greatly reduce the need for task-specific modeling expertise, training compute, and custom data annotation for image segmentation.
SAM has learned a general notion of what objects are, and it can generate masks forβ¦
