MOSAIKS models comparison with Resnet and pre-trained CNN models
In this post, you will learn about the framework, MOSAIKS (Multi-Task Observation using Satellite Imagery & Kitchen Sinks) which can be used to create machine learning linear regression models for climate change. Here is the list of few prediction use cases which has already been tested with MOSAIKS and found to have high model performance:
MOSAIKS provides a set of features created from Satellite imagery dataset. We are talking about 90TB of data gathered per day from 700+ satellites. These features can be combined with machine learning algorithms to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions. Combining satellite imagery with machine learning is also termed as SIML approach.
The set of features generated using MOSAIKS can be merged spatially with the labels. Thereafter, you can run a linear regression of your labels on the MOSAIKS features, measure performance and use the model for making predictions in your area of interest.
The algorithmic component of the MOSAIKS system is built upon the random convolutional features (RCF) algorithm.
The MOSAIKS features facilitates a generalizable and accessible approach to machine learning with global satellite imagery. The picture below represents how MOSAIKS can be used to perform different predictions tasks related to solving problems in the areas of socioeconomic and environmental issues.
Fig 1. MOSAIKS used to solve different socioeconomic and environmental problems
Pay attention to some of the following in above picture:
Fig 2. MOSAIKS – Merging unsupervised features with labels
MOSAIKS is tested to achieve comparable performance with respect to a fine-tuned ResNet-18 at a fraction of the computational cost. The picture below shows the comparison of MOSAIKS trained regression models against ResNet-18 and pre-trained CNN.
Fig 3. MOSAIKS models performance and computation comparison with Resnet-18 and pre-trained CNN models
There is a limited access to skills, data, compute and resources in relation to understanding and processing data gathered from satellites in form satellite imagery. Transforming the satellite imagery data into relevant statistics is costly and requires skills which may not be available with many. This is where MOSAIKS come as a boon. It converts the satellite imagery data to K-dimensional features set which can be merged with user-chosen labels and used to train the regression models for making predictions.
As mentioned above, MOSAIKS provides the K-dimensional features set which can be used to solve training regression models related to different problems related to social-economic and environmental factors. Here are the steps which can be used to solve problems related to climate change:
Download MOSAIKS features from our API for the areas where you have labels
Merge the features spatially with your labels.
Run a regression of your labels on the MOSAIKS features
Evaluate performance
Make predictions
Here are some important pages which can help you get started:
Here is a great youtube video on MOSAIKS
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