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Segmentação de Imagens Satélites para Análise da Invasão Humana na Amazônia

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Final project Convolutional Neural Network Course (IA368Z, Unicamp)

Planet: Understanding the Amazon from Space

Use satellite data to track the human footprint in the Amazon rainforest

Note: this project is based on an Kaggle Competition

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Description

Every minute, the world loses an area of forest the size of 48 football fields. And deforestation in the Amazon Basin accounts for the largest share, contributing to reduced biodiversity, habitat loss, climate change, and other devastating effects. But better data about the location of deforestation and human encroachment on forests can help governments and local stakeholders respond more quickly and effectively.

Planet, designer and builder of the world’s largest constellation of Earth-imaging satellites, will soon be collecting daily imagery of the entire land surface of the earth at 3-5 meter resolution. While considerable research has been devoted to tracking changes in forests, it typically depends on coarse-resolution imagery from Landsat (30 meter pixels) or MODIS (250 meter pixels). This limits its effectiveness in areas where small-scale deforestation or forest degradation dominate.

Furthermore, these existing methods generally cannot differentiate between human causes of forest loss and natural causes. Higher resolution imagery has already been shown to be exceptionally good at this, but robust methods have not yet been developed for Planet imagery.

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Segmentação de Imagens Satélites para Análise da Invasão Humana na Amazônia

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