Satellite image deep learning Field boundary detection with Segment Anything
In this episode I caught up with Samuel Bancroft to learn about segmenting field boundaries using Segment Anything, aka SAM. SAM is a foundational model for vision released by Meta, which is capable of zero shot segmentation. However there are many open questions about how to make use of SAM with remote sensing imagery.
In this conversation, Samuel describes how he used SAM to perform segmentation of field boundaries using Sentinel 2 imagery over the UK. His best results were obtained not by fine tuning SAM, but by carefully pre-processing a time series of images into HSV colour space, and using SAM without any modifications. This is a surprising result, and using this kind of approach significantly reduces the amount of work necessary to develop useful remote sensing applications utilising SAM. You can view the recording of this conversation on YouTube here
- https://github.com/Spiruel/UKFields
Bio: Sam Bancroft is a final year PhD student at the University of Leeds. He is assessing future food production using satellite data and machine learning. This involves exploring new self- and semi- supervised deep learning approaches that help in producing more reliable and scalable crop type maps for major crops worldwide. He is a keen supporter in democratising access to models and datasets in Earth Observation and machine learning.
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