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During my time at Notilyze I developed a quantitative model for one of their biggest clients as part of my thesis for the master Quantitative Finance. 

In the previous article about contrast stretching, we explored percentile contrast stretching and how to apply this to obtain better performance in object detection models. Percentile contrast stretching is also called (histogram) normalization, as we normalize the range of the pixel intensities.

An important part of training neural networks is preprocessing of the input. A lot of performance gain can be obtained by carefully examining, cleaning and transforming the input data.

As stated in our news post, we are going to make a model to extract information from satellite images to help estimating the number of refugees in refugee camps in Nigeria. In this post we will dive some deeper into this goal. We will answer the questions “How do we want to make this model work?” and “How is this going to help in estimating the number of refugees?”