An important part of EO/GIS is to detect infrastructure, as roads, for different purposes.
We have seen in the past years, with the increase of high resolution satellites and deep learning algorithms, an increase in solutions for image labelling based on high resolution datasets. Now, we still have much unexplored low resolution data that it is cheaper and might be more useful in some cases.

What I propose in this proof of concept project is to use transfer learning by training a model on an already labelled dataset of 1m resolution and then test it on 30m resolution imagery. …


CNN, DeepLearning, Tensorflow

Quoting *1: Georeferencing is the process of taking a digital image, it could be an airphoto, a scanned geologic map, or a picture of a topographic map, and adding geographic information to the image so that GIS or mapping software can ‘place’ the image in its appropriate real world location.

There are few ways of achieving this, though we can split them in two categories: “manual” and “automated”. When comes to manual — it means that for two images(one is part of another), one has to pick manually the points of intersection(same points found in both images)…


Synthetic Aperture Radar Satellite Imagery

“SAR is a form of radar that is used to create two-dimensional images or three-dimensional reconstructions of objects, such as landscapes. SAR uses the motion of the radar antenna over a target region to provide finer spatial resolution than conventional beam-scanning radars.” Wikipedia

Synthetic Aperture Radar can be quite a great tool since it is used to see through clouds, and does not depend on day or night.

My purpose for this test is to understand if satellite SAR imagery can obtain similar results as with optical images when it comes to detecting anomalies.


Unsupervised Learning

Synthetic Aperture Radar can be quite a great tool since it is used to see through clouds, and does not depend on day or night.

“SAR is a form of radar that is used to create two-dimensional images or three-dimensional reconstructions of objects, such as landscapes. SAR uses the motion of the radar antenna over a target region to provide finer spatial resolution than conventional beam-scanning radars.” Wikipedia

My purpose for this test is to understand if SAR imagery can obtain similar results as optical images when it comes to clustering.


Convolutional Neural Networks

“Forests cover 30% of the world’s land area.”


Deep convolutional generative adversarial networks, GANs

I have been recently inspired by Omdena’s last summer's challenge, to detect anomalies on Mars surface, and thought to spot, using GANs, any type of technological signature detected around forests for deforestation purposes.


Clustering for Satellite Imagery

As you have seen in my previous work for classifying objects in satellite imagery, deep learning is used, and it does achieve good results, though it has a high dependency on the amount of available data, and more exactly labeled data.

Remote sensing creates a huge amount of data every day which mostly lacks annotations. The data complexity and the lack of a well-defined typology make it impossible to use supervised tools. And here is where clustering methods come in.


Classifying Wind Turbines in Satellite Imagery with use of Machine Learning Neural Networks and calculating their Orientation with Computer Vision

Modern wind turbines are designed to automatically orient themselves to face the direction of the wind, in order to maximise power generation. A ‘black box’ collects this orientation data and transmits the telemetry back to the wind turbine operators. This telemetry is crucial for revealing faults and whether the turbine requires maintenance.

During the course of a wind turbine’s lifetime, these black boxes can fail and prove costly to replace. Sometimes the telemetry is inaccurate, and operators wish to validate the accuracy of these orientation measurements by comparing them to other data.


and.. pandas!

The new year has started and we are all already in the fever of doing things. So I thought I’ll have a fun post for this weekend!

And it will be, of course, about pandas, solar farms and time-lapses!

How I came about it, just by keeping an eye on what happens in the renewables area, and I quote from the article:

In 2017, the groups built a 248-acre solar power plant in Daton, China, that looks from above like two smiling pandas. …


How to deploy a machine learning multi-class classification Keras model for predicting voice sentiment.

Part I:
build the NN model, the Flask application and include Html, CSS, js for the voice recorder.

Part II:
how to choose the right configuration for your Docker image.

Part III:
deploy on AWS Elastic Beanstalk and how to adjust the voice recorder.

All the above contains common troubleshoots as well. Some of them you’ll find on my StackOverflow.

Part I:
Why voice sentiment? Due to exponential growth in technology in the past decade, lifestyle has changed and people demand faster and easier to use services. Many companies do renew contracts and get customer agreement legally valid only through a…

Daniel Moraite

My passion for technology and previous roles inspired me to get closer to the practical side of things and I started studying data science and coding on my own.

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