Training Convolutional Neural Networks On Satellite Image Data

Ahmet Taspinar builds a neural net which detects roads in satellite images:

Next we will determine the contents of each tile image, using data from the NWB Wegvakken (version September 2017). This is a file containing all of the roads of the Netherlands, which gets updated frequently. It is possible to download it in the form of a shapefile from this location.
Shapefiles contain shapes with geospatial data and are normally opened with GIS software like ArcGIS or QGIS. It is also possible to open it within Python, by using the pyshp library.

This is a pretty lengthy and interesting tutorial.  H/T Data Science Central

Related Posts

Using R To Hit Azure ML From Power BI

Leila Etaati shows how you can use R to hit an Azure ML endpoint to populate a data set in Power BI: You need to create a model in Azure ML Studio and create a web service for it. The traditional example in Predict a passenger on Titanic ship is going to survived or not? […]

Read More

Image Clustering With Keras And R

Shirin Glander shows us how to use R to extract learned features from Keras and cluster those features: For each of these images, I am running the predict() function of Keras with the VGG16 model. Because I excluded the last layers of the model, this function will not actually return any class predictions as it would normally […]

Read More

Categories

December 2017
MTWTFSS
« Nov Jan »
 123
45678910
11121314151617
18192021222324
25262728293031