FOREST MONITORING IN GUATEMALA USING SATELLITE IMAGERY AND DEEP LEARNING

被引：0

作者：

Wyniawskyj, Nina Sofia ^{[1
]}

Napiorkowska, Milena ^{[1
]}

Petit, David ^{[1
]}

Podder, Pritimoy ^{[1
]}

Marti, Paula ^{[1
]}

机构：

[1] Deimos Space UK Ltd, Harwell, Berks, England

来源：

2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年

关键词：

deforestation; change detection; machine learning; remote sensing; Guatemala; convolutional neural network; DEFORESTATION;

D O I：

10.1109/igarss.2019.8899782

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Forests cover 34% of Guatemala. The Guatemalan government have taken proficient actions in past decades to reduce deforestation and are looking toward new space technologies to improve forestry monitoring. This paper demonstrates the ability to automatically detect pixel-level changes in satellite images of forested areas that can be used to assist Guatemalan agencies, using satellite imagery from the Copernicus program and specially-developed deep learning algorithms for image segmentation.

引用

页码：6598 / 6601

页数：4

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