Downstream lingering attention transformer network (DsLATNet) for land use land cover classification: A bicolor deep learning framework

Author:

Anitha V.,Manimegalai D.,Kalaiselvi S.

Publisher

Elsevier BV

Reference58 articles.

1. Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data;Ahmad;Environ. Sci. Pollut. Res.,2023

2. Landuse/Landcover mapping and monitoring using Remote sensing and GIS with environmental integration;Hamud,2019

3. Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India;Mishra;Egypt. J. Remote Sens. Space Sci.,2020

4. Object-based classification framework of remote sensing images with graph convolutional networks;Zhang;IEEE Geosci. Remote Sens. Lett.,2021

5. Accuracy improvements to pixel-based and object-based LULC classification with auxiliary datasets from Google Earth engine;Qu;Remote Sens.,2021

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