HSG-AIML/GDA
Code repository for "Parameter Efficient Self-supervised Geospatial Domain Adaptation", CVPR 2024
This project helps remote sensing analysts adapt existing AI models to new types of satellite or aerial imagery without needing massive amounts of new labeled data. You provide a pre-trained geospatial foundation model and unlabeled images from your specific area or sensor. The output is a fine-tuned model ready to perform tasks like land cover classification or object detection on your new imagery.
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Use this if you need to apply a pre-trained geospatial AI model to different types of satellite imagery, perhaps from a new sensor or geographic region, where you have plenty of unlabeled data but very little labeled data for your specific task.
Not ideal if you are a casual user looking for a ready-to-use application, as this requires familiarity with machine learning workflows and dataset preparation.
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Python
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Jul 29, 2024
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