A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data

A Weakly Supervised and Self-Supervised Learning Approach for Semantic Segmentation of Land Cover in Satellite Images with National Forest Inventory Data

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convol...

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Bibliographic Details
Main Authors: Daniel Moraes, Manuel L. Campagnolo, Mário Caetano
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Remote Sensing
Subjects:
land cover
deep learning
convolutional neural networks
masked autoencoder
national forest inventory
sentinel-2
Online Access:https://www.mdpi.com/2072-4292/17/4/711
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