# Identifying Land Patterns from Satellite Imagery in Amazon Rainforest using Deep Learning

Source: [Identifying Land Patterns from Satellite Imagery in Amazon Rainforest using Deep Learning](<https://www.sota2.com/research/paper/68fa64c71e209131cea068ca>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:36.769Z

Authors: Somnath Rakshit, Soumyadeep Debnath, Dhiman Mondal

Published: 2018-09-02T14:06:39.000Z

## Abstract

The Amazon rainforests have been suffering widespread damage, both via natural and artificial means. Every minute, it is estimated that the world loses forest cover the size of 48 football fields. Deforestation in the Amazon rainforest has led to drastically reduced biodiversity, loss of habitat, climate change, and other biological losses. In this respect, it has become essential to track how the nature of these forests change over time. Image classification using deep learning can help speed up this process by removing the manual task of classifying each image. Here, it is shown how convolutional neural networks can be used to track changes in land patterns in the Amazon rainforests. In this work, a testing accuracy of 96.71% was obtained. This can help governments and other agencies to track changes in land patterns more effectively and accurately.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1809.00340>)

[PDF](<https://arxiv.org/pdf/1809.00340.pdf>)