Journal Article10.1016/j.scitotenv.2022.153559
Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects.
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TL;DR: In this article , the authors review principles and methods of land-use and land-cover change (LULCC) modeling using machine learning and beyond, such as traditional cellular automata (CA), and examine the characteristics, capabilities, limitations, and perspectives of machine learning.
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About: This article is published in Science of The Total Environment. The article was published on 01 Jan 2022. The article focuses on the topics: Medicine & Land cover.
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Integrated Deep Learning and Genetic Algorithm Approach for Groundwater Potential Zone Prediction Incorporating Cmip6 Gcm: Unveiling Synergies for Enhanced Water Resource Management
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References
Fully Convolutional Networks for Semantic Segmentation
TL;DR: Fully convolutional networks (FCN) as mentioned in this paper were proposed to combine semantic information from a deep, coarse layer with appearance information from shallow, fine layer to produce accurate and detailed segmentations.
10.6K
Classification of hyperspectral remote sensing images with support vector machines
Farid Melgani,Lorenzo Bruzzone +1 more
TL;DR: This paper addresses the problem of the classification of hyperspectral remote sensing images by support vector machines by understanding and assessing the potentialities of SVM classifiers in hyperdimensional feature spaces and concludes that SVMs are a valid and effective alternative to conventional pattern recognition approaches.
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.
Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009
Maosheng Zhao,Steven W. Running +1 more
TL;DR: Satellite data used to estimate global terrestrial NPP over the past decade found that the earlier trend has been reversed and that NPP has been decreasing, and combined with climate change data suggests that large-scale droughts are responsible for the decline.
2.5K
Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art
Liangpei Zhang,Lefei Zhang,Bo Du +2 more
TL;DR: A general framework of DL for RS data is provided, and the state-of-the-art DL methods in RS are regarded as special cases of input-output data combined with various deep networks and tuning tricks.
2.1K