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Environmental Change Detection Using High-Resolution Satellite Imagery

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Overview

1,200 miles above our heads are hundreds of satellites in low earth orbit traveling at 17,000 miles an hour that are taking high-resolution images of the earth. The aim of this is to understand our world in ever-increasing detail.

Environmental degradation is one area of focus within satellite imagery that is aiming to measure the impact of deforestation as well as natural phenomena, such as wildfires on our environment.

The dangers of mass land-use changes are obvious to all – biodiversity loss and species extinction, in addition to increased flooding and other extreme weather events being just a few.

The Client is a U.S. company building a model to detect changing environmental landscapes. Their data scientists needed to segment large satellite images so that their model could identify different types of land use.

Data Annotation

Image segmentation

Industry

Geospatial

Headquarters

London, UK

Company Size

250+

Challenge

Aya Data’s challenge was to segment roads, rivers, lakes, residential use, industrial use, and natural terrain into different classes within 2,000 ultra-high resolution satellite images of South America covering several years.

Identifying human-led deforestation was a particular challenge within this project – this required expertise in agronomy and forestry, and a deep understanding of the environmental context of the images.

The complexity and scale of each image meant that the images had to be viewed in part and as a whole to provide perspective and context to larger-scale objects such as man-made rivers originating at hydroelectrical plants.

Solutions

Aya Data used its image segmentation experts in Ghana to accurately label 2,000 ultra-high resolution satellite images. Teams experienced in labeling satellite imagery were critical to delivering the project to the right degree of accuracy within the given timeframe, at a much lower cost than other vendors.

Results

The images labeled by Aya Data were used to accurately train a custom computer vision model to detect the changing land uses of regions of South America.

The resulting model will be used in an upcoming research paper, which will guide policy change and investment regarding environmentally friendly farming techniques in South America.