Dhruv Sheth / terrain-classification-spresense

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

About this project


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.

This project proposes, a faster, accurate, cheaper method to satellite terrain classification on Sony Spresense using EdgeImpulse


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.


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
Image training data NPY file 650 windows
Image training labels NPY file 650 windows
Image testing data NPY file 150 windows
Image testing labels NPY file 150 windows
Transfer learning model TensorFlow Lite (float32) 427 KB
Transfer learning model TensorFlow Lite (int8 quantized) 285 KB
Transfer learning model TensorFlow SavedModel 685 KB


Data collected
800 items

Project info

Project ID 70098
Project version 1