Dhruv Sheth / cloud-classification-spresense

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

About this project

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A few days ago Sony announced Spresense capabilities and test procedures on how Spresense will be useful in Space Applications and Satellite Monitoring Systems. While currently this board will be used on ongoing missions or to develop existing technology systems, it might prove of great help in expanding core research in the EdgeAI sector. With opening avenues in low power consumption monitoring systems and software under constrained computational power, further this will help in democratizing Aerospace based terrain monitoring or in that matter, even space exploration.

If you're a Researcher working in this field, you might have read about different prototypes on reconstruction of areas obscured by clouds using Contextualized Auto Encoder Neural Networks (another branch of GANs). But this opens more room for thought. Are these Algorithms efficient and practical or computationally inexpensive to be deployed on Satellites which are capable of running on low power? Looking at the current trends, the answer is No and this methodology is not sustainable for couple of years coming forth unless we undergo a microprocessor breakthrough capable for just that. Also, these algorithms which reconstruct these images are dependent on the data fed to the Model and learns to reconstruct the information which is already available. This leaves no room to the unobserved image information which is what the Earth Observation Departments really require. And finally, the computational cost for capturing two subsequent images of the same region at different points in time is far less than what the algorithm requires to process 1 reconstruction, which brings us to our Conclusion. Low power Embedded Machine Learning which is the simplest yet the most effective solution to the evident problem.

Creating your first impulse (100% complete)

Acquire data

Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.

Design an impulse

Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.

Deploy

Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.

Download block output

Title Type Size
Neural Network (Keras) model TensorFlow Lite (float32) 384 KB
Neural Network (Keras) model TensorFlow Lite (int8 quantized) 107 KB
Neural Network (Keras) model TensorFlow Lite (int8 quantized with float32 input and output) 108 KB
Neural Network (Keras) model TensorFlow SavedModel 370 KB

Summary

Data collected
1,009 items

Project info

Project ID 70094
Project version 2