The Camp Fire grew to become the destructive wildfire

Numerous industries benefit from remote satellite sensing

Applications Area

Insurance

Type measurements

Burn delineation

Deforestation

etc...

Agriculture

challenge

However, clouds and the shadows they cast on the Earth often obscure key features

Difficulty

Overuse

Value

67% of the Earth's surface is typically covered by clouds at any one time.

Therefore, a significant part of satellite images contain only partial data.

This results in considerable increases in time and costs to sort cloudy for non-cloudy images.

how it works

neural network & human brain alliance

U-Net

Human

SoftServe employed a popular deep learning architecture called U-Net for the task of recognizing different labels in a sky/cloud image.

 

We trained an architecture on 256x256 px cropped images to reach higher accuracy. The output of the U-Net model is a probabilistic mask, wherein each pixel has a softargmax value in the range [0, 1]. This value indicates the degree of cloudiness of the pixel in the image. However, the success of any machine learning application depends heavily on the quality and size of the training data. 

SoftServe’s R&D team prepared the high-quality labeled dataset from scratch. Clouds and their cast shadows were then accurately labeled on the satellite images with different landscapes. It consists of a thousand hand-labeled 512x512 patches of Sentinel-2 images. Each labeled with one of the following three classes: clear (land), cloud, shadow. Additionally, the following were used for training: Red, Green, Blue and NIR channels.

 

VALUE PROPOSITION

relevant information with little effort

SoftServe’s machine learning algorithm can sort satellite images based on the level of cloud/shadow obstruction to provide the most relevant and data rich images with little needed human effort.

Universal

Effective

Adjustable

Can be used with different satellites and platforms

Detects both clouds and their cast shadows

Easy to re-train on any landscape or environment

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