Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects-Reference-Cited by-同舟云学术

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Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Published:2022-05 Issue: Volume:822 Page:153559
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Wang Junye,Bretz Michael,Dewan M. Ali Akber,Delavar Mojtaba Aghajani

Funder

Alberta Ministry of Economic Development and Trade

Publisher

Elsevier BV

Subject

Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering

Reference193 articles.

1. Land-use/cover classification in a heterogeneous coastal landscape using rapid eye imagery: evaluating the performance of random forest and support vector machines classifiers;Adam;Int. J. Remote Sens.,2014

2. Cropping pattern mapping in an agro-natural heterogeneous landscape using Sentinel-2 and Sentinel-1 satellite datasets;Aduvukha;Agriculture,2021

3. Intelligent Techniques for Data Science;Akerkar,2016

4. A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery;Alhassan;Neural Comput. Applic.,2020

5. Using neural networks and cellular automata for modelling intra-urban land-use dynamics;Almeida;Int. J. Geogr. Inf. Sci.,2008

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3. Applying object-based approach to monitor urban expansion in Ha Long city, Vietnam, during 2000-2023 from multi-date Landsat satellite imagery;European Journal of Remote Sensing;2024-09-04

4. Towards practical artificial intelligence in Earth sciences;Computational Geosciences;2024-09-02

5. Deep learning for urban land use category classification: A review and experimental assessment;Remote Sensing of Environment;2024-09

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