Applying Deep Learning Methods for a Large-Scale Riparian Vegetation Classification from High-Resolution Multimodal Aerial Remote Sensing Data

Applying Deep Learning Methods for a Large-Scale Riparian Vegetation Classification from High-Resolution Multimodal Aerial Remote Sensing Data

The unique vegetation in riparian zones is fundamental for various ecological and socio-economic functions in these transitional areas. Sustainable management requires detailed spatial information about the occurring flora. Here, we present a Deep Learning (DL)-based approach for processing multimod...

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Bibliographic Details
Main Authors: Marcel Reinhardt, Edvinas Rommel, Maike Heuner, Björn Baschek
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Remote Sensing
Subjects:
riparian zone
river
airborne imagery
semantic segmentation
deep learning
convolutional neural networks
Online Access:https://www.mdpi.com/2072-4292/17/14/2373
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https://www.mdpi.com/2072-4292/17/14/2373

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