Deforestation monitoring based on machine learning techniques
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 th...
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| Format: | master thesis |
| Publication Date: | 2024 |
| Country: | España |
| Institution: | Universitat Politècnica de Catalunya (UPC) |
| Repository: | UPCommons. Portal del coneixement obert de la UPC |
| Language: | English |
| OAI Identifier: | oai:upcommons.upc.edu:2117/421466 |
| Online Access: | https://hdl.handle.net/2117/421466 |
| Access Level: | Open access |
| Keyword: | Machine learning Artificial intelligence Remote sensing Machine Learning Earth observation Artificial Intelligence Aprenentatge automàtic Intel·ligència artificial Teledetecció Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció |
| Summary: | 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. |
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