2026-04-13T13:24:27Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/4214662025-07-17T15:46:07Zcom_2072_1033col_2072_452951

00925njm 22002777a 4500 dc Manresa Román, María-Isabel author 2024-05-29 Machine Learning (ML) represents one of the most dynamical fields in contemporary technology. In conjunction with remote sensing for Earth observation data, it enables groundbreaking advancements in areas like environmental monitoring and disaster response, revolutionizing the analysis and use of this data. This project has been carried out at UPC and Tracks CO2, a startup company specialized in Artificial Intelligence (AI) and using remote sensing for Earth observation data to monitor forests. In this case, the project proposal is to build a change detection system that focuses on classifying types of changes in forests during different instances of time with the final objective of monitoring deforestation. For this project, we have built the databases, and trained random forest, XGBoost and Multilayer Perceptron \ac{MLP} models to reach a 0,486 accuracy in validation and test when using specific classes. The accuracy goes up to 0,705 when merging classes to detect Forest growth or forest shrinkage. Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció Machine learning Artificial intelligence Remote sensing Machine Learning Earth observation Remote sensing Artificial Intelligence Aprenentatge automàtic Intel·ligència artificial Teledetecció Deforestation monitoring based on machine learning techniques Deforestation monitoring based on machine learning techniques