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In this work, we propose a contrastive learning architecture, called Multimodal SuperCon, for classifying drivers of deforestation in Indonesia using satellite images obtained from Landsat 8. ... Deforestation is one of the contributing factors to climate change. ... We thank the support of Tokopedia-UI AI Center of Excellence, Faculty of Computer Science, University of Indonesia, for providing access to the NVIDIA DGX-A100 to run the experiments. ...arXiv:2207.14656v1 fatcat:gxdw22y7evc4zkwlxnovpkmnaa
With a focus on Sustainable Development Goals 14 (Life below Water) and 15 (Life on Land), this paper explores the opportunities of Artificial Intelligence applications in various domains of wildlife, ... For this purpose, we develop a conceptual framework on the basis of a comprehensive review of the literature and examples of Artificial Intelligence-based approaches to protect endangered species, monitor ... Example 12: ForestNet is a deep learning model developed to automatically identify and classify the drivers of primary forest loss in Indonesia, using satellite imagery  . ...doi:10.3390/su14041979 fatcat:bsng537lsjeuxlcpmv5lndtj2m
In this article, we approach deforestation as a multilabel classification (MLC) problem in an endeavor to capture the various relevant land uses from satellite images. ... learning models utilized for deforestation detection. ...  develop a DL model called ForestNet to classify the drivers of primary forest loss in Indonesia, using Landsat 8 satellite images. ...doi:10.1109/tnnls.2022.3144791 pmid:35108212 fatcat:x6vbssi7zzedfbme5j5kpe5i64
In this thesis, we first tackle the task of creating a pipeline for preprocessing said satellite imagery. ... Next, we will perform multi-label classification in an attempt to describe the content of the images in terms of the factors that contribute to the deforestation, using a set of tags. ... Acknowledgements I would like to express my gratitude to my supervisor Athanasios Voulodimos for providing me with a great deal of support and assistance throughout the learning process of this thesis. ...doi:10.26265/polynoe-244 fatcat:lgnfdardbfg7pkkzbv2xminuqq