Machine Learning
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An Introduction to Satellite Imagery and Machine Learning
Today, the availability of satellite imagery still far outpaces our capacity to analyze it, but machine learning and tools like Raster Vision are helping.
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An Introduction to Satellite Imagery and Machine Learning
Today, the availability of satellite imagery still far outpaces our capacity to analyze it, but machine learning and tools like Raster Vision are helping.
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Raster Vision 0.9 Release Candidate
This release of Raster Vision includes bug fixes, an easier setup, improved performance, and the ability to train models off of labels in OSM.
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E84 Lab Notes: Machine Learning with SageMaker
In a previous post we showed how the E84 R&D team used RoboSat by Mapbox to prepare training data, train a machine learning model, and run predictions on new satellite imagery. In this example, we’re going to use the same imagery source and label data as a proxy for data produced by our AWS disaster…
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E84 Lab Notes: Machine Learning with RoboSat
Recently, the E84 R&D team has been experimenting with machine learning pipelines and identifying potential use cases. There are a lot of new and exciting tools out there and we’re interested in exploring what’s available, particularly tools related to satellite and aerial imagery (one of our specialties). Mapbox‘s RoboSat was released earlier this year and…
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Evaluating Machine Learning Models in R: Predicting Marine Debris
Evaluating machine learning models in R with a focus on how to handle biased and imperfect data, specifically volunteer collected marine debris data.
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What is Spark?
We describe a group research project in which we worked to evaluate and port existing machine learning and modeling functionality in HunchLab from R to Spark.
