Kapak Resmi

Deep Learning and Transformers’ Methods Applied to Remotely Captured Data

The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has mainly been driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep n...

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Materyal Türü: Online
Dil:İngilizce
Baskı/Yayın Bilgisi: MDPI - Multidisciplinary Digital Publishing Institute 2024
Konular:
Remote Sensing
Super-Resolution
Deep Learning
Ship Detection
Satellite Imaging
Transformer Models
Image Classification
Terrain Analysis
LiDAR Data
Point Cloud Transformer
thema EDItEUR::R Earth Sciences, Geography, Environment, Planning
thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography
thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography::RGW Geographical information systems, geodata and remote sensing
Online Erişim:ONIX_20240704_9783725805853_60
Etiketler: Etiketle
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Özet:The areas of machine learning and deep learning have experienced impressive progress in recent years. This progress has mainly been driven by the availability of high processing performance at an affordable cost and a large quantity of data. Most state-of-the-art techniques today are based on deep neural networks or the more recently proposed transformers. This progress has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently. Among the various research areas that have been significantly impacted by this progress is the processing of remotely captured data such as airborne and spaceborne passive and active imagery, underwater imagery, mobile mapping data, etc. This collection gathered cutting-edge contributions from researchers using deep learning and transformers for remote sensing and for processing remotely captured data.

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