• Anusha R Shenoy
• Mahathi N
• Ranjitha L
• Impu
• Swetha P M

The Amazon ecological system has been studied several times over the years, and possibilities for such research have only increased since the prospect of utilising satellite imagery for the purpose opened up. One of the major concerns here is the alarming rate at which the forests in the rainforest is being chopped off, even to convert the land into other commercially profiting establishments such as farms and plantations. With the advent of deep neural networks and transfer learning, breaking down images taken even from great altitudes to be able to decipher the change in landforms from one piece of land to another became a credible solution to spot deforestation through an entirely automated process. A reliable and acceptably accurate Convolutional Neural Network (CNN) should not only enable the administration to nab perpetrators, warn the general public of the impending nature crisis that the world is headed towards and distinguish what the newly deforested regions are being used for - coffee plantations, roads, human settlements, or date palm plantations as the commonly observed patterns suggest. A successful solution of this nature can then be replicated for natural landscapes of all kinds that are being subjected to exploitation across the world.

Deep Learning Architecture, Amazon Rainforest, Deforestation Detection, Convolutional Neural Network

"Analysis of Deep Learning Architectures in Deforestation Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.g760-g767, June-2022, Available :http://www.jetir.org/papers/JETIR2206690.pdf

"Analysis of Deep Learning Architectures in Deforestation Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppg760-g767, June-2022, Available at : http://www.jetir.org/papers/JETIR2206690.pdf