Literature review on neural net architectures

[martinfleis]

Local climate zones

Re-ResNet	From scratch	Taubenböck et al 2020	https://www.sciencedirect.com/science/article/pii/S0264275120301347#s0015
LCZNet	From scratch	Liu and Shi 2020	https://www.sciencedirect.com/science/article/pii/S0924271620301052?casa_token=vQctJ83w0A4AAAAA:OL4RVN1D6u1u7tGWr9XHfqnFje487zJ-go5N7P41fEwKrjUDrLvfiXowLPuC6_zGFm8h9QQ
ResNet	From scratch	Qui et al 2020	https://ieeexplore.ieee.org/abstract/document/8951229
Re-ResnNet; st-ResNet	From scratch	Qiu et al 2019	https://ieeexplore.ieee.org/abstract/document/8898223?casa_token=TAVPq_AhFeIAAAAA:1qG8wbuhM21jvECJJCjk3ERMgGPj039X8NegWIFvtuBYsxCTCwMXVvkOA1gDvprPxerZXhg
modified ResNet	From scratch	Zhu et al 2022	https://www.sciencedirect.com/science/article/pii/S0034425721005149

Land use/ land cover

VGG, ResNet, GoogLeNet	Imagenet	Alhassan et al 2020	https://link.springer.com/article/10.1007/s00521-019-04349-9#Sec5
UNet	from scratch	Karra et al 2021	https://ieeexplore.ieee.org/abstract/document/9553499?casa_token=eR90aq57UioAAAAA:dt2JW4sMT9WaYgI9Rjw2FDUyFtFTsNuU4vPXt9KxXo-Jt7_1tAOFlT9BFHUbAQBAWMrZ-Ro
VGG16	Imagenet	Srivastava et al 2019	https://www.sciencedirect.com/science/article/pii/S0034425719301579?via%3Dihub#bb0205
custom	from scratch	Sharma et al 2017	https://www.sciencedirect.com/science/article/pii/S0893608017301806#sec4
custom	from scratch	Wang et al 2018	https://www.mdpi.com/1424-8220/18/3/769/htm#overview
AlexNet, GoogleNet, VGG	imagenet	Xie et al 2019	https://ieeexplore.ieee.org/abstract/document/8699111?casa_token=xb-UTbyfvP0AAAAA:HUGwJCG1_cTkNPXxbVKQsv70NZeZd12mXBDKpDKwk3E1z36-BiIji5uL1jlc4JG1h1TULQ3o4ck
custom	Imagenet	Othman et al 2017	https://ieeexplore.ieee.org/abstract/document/7917346?casa_token=Ced5jTmzjfEAAAAA:wte-jF3L3UeaEqS_wNzZ1t_je_Hs1FDY7FvZtaZVKZBrF_NvHxk2oYuxuZ8rgAumTSNhwa4iEdI
AlexNet, VGG16	Imagenet	He et al 2018	https://ieeexplore.ieee.org/document/8408558
AlexNet, GoogLeNet, VGG16	Imagenet	Cheng 2018	https://ieeexplore.ieee.org/document/8252784
Overfeat (AlexNet)	Imagenet	Marmanis 2015	https://ieeexplore.ieee.org/abstract/document/7342907?casa_token=TvFRCV4x-eIAAAAA:q6PkZNXBoOwfpai7KROMel8kvl8uNOluY11J0Rt7WrZ-gB1X-jXUwvTLONvBnJqKi38gw7hKFiw
OverFeat, AlexNet, CaffeNet, GoogLeNet, Vgg16, PatreoNet	Imagenet	Nogueira et al 2017	https://www.sciencedirect.com/science/article/pii/S0031320316301509#s0055
custom	from scratch	He et al 2018	https://ieeexplore.ieee.org/abstract/document/8439081?casa_token=gOzusHT4BmkAAAAA:8_OrTxx3QSod5UUTi_jBw4bKXm_y6OWrrNL9wi0oGcYaDv_cPVL2BoH0RZXaf06Ay6XMXv0JYGY
custom	from scratch	Zhu et al 2018	https://ieeexplore.ieee.org/document/8361481
AlexNet, CaffeNet, VGG-M, VGG-S, VGG-F, VGG-VD16, and VGG-VD19	Imagenet	Li et al 2017	https://ieeexplore.ieee.org/abstract/document/7959553?casa_token=fLbu2LgibVkAAAAA:JtCdCoYgYmldl5PFrl75p7IZJxiKlNwJnH1P-vbOS9O_1JC5O9pqX1wFhs223w0NtPDZebHf7gE

Other

VGG16 feature extraction	Imagenet	ŠĆEPANOVIĆ, Law…	https://arxiv.org/pdf/2102.00848.pdf
Inception V3; VGG16	Imagenet	Chew et al 2018	https://ij-healthgeographics.biomedcentral.com/articles/10.1186/s12942-018-0132-1#Sec5

[darribas]

Adding this to remember to include it if possible:

https://doi.org/10.1016/j.isprsjprs.2019.04.015

Deep learning in remote sensing applications: A meta-analysis and review

Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing image analysis over the past few years. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. Initially, a meta-analysis was conducted to analyze the status of remote sensing DL studies in terms of the study targets, DL model(s) used, image spatial resolution(s), type of study area, and level of classification accuracy achieved. Subsequently, a detailed review is conducted to describe/discuss how DL has been applied for remote sensing image analysis tasks including image fusion, image registration, scene classification, object detection, land use and land cover (LULC) classification, segmentation, and object-based image analysis (OBIA). This review covers nearly every application and technology in the field of remote sensing, ranging from preprocessing to mapping. Finally, a conclusion regarding the current state-of-the art methods, a critical conclusion on open challenges, and directions for future research are presented.