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A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture
v1v2v3 (latest)

A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture

3 October 2022
Brandon Victor
Zhen He
Aiden Nibali
ArXiv (abs)PDFHTML

Papers citing "A systematic review of the use of Deep Learning in Satellite Imagery for Agriculture"

27 / 27 papers shown
Title
A Sentinel-2 multi-year, multi-country benchmark dataset for crop
  classification and segmentation with deep learning
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learning
Dimitrios Sykas
Maria Sdraka
Dimitrios Zografakis
Ioannis Papoutsis
101
39
0
02 Apr 2022
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite
  Time Series
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time Series
Vivien Sainte Fare Garnot
Loic Landrieu
N. Chehata
78
97
0
14 Dec 2021
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift
  Estimation
TimeMatch: Unsupervised Cross-Region Adaptation by Temporal Shift Estimation
Joachim Nyborg
Charlotte Pelletier
Sébastien Lefèvre
Ira Assent
OODAI4TS
73
42
0
04 Nov 2021
Crop Rotation Modeling for Deep Learning-Based Parcel Classification
  from Satellite Time Series
Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series
Félix Quinton
Loic Landrieu
48
12
0
15 Oct 2021
Domain-Adversarial Training of Self-Attention Based Networks for Land
  Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery
Domain-Adversarial Training of Self-Attention Based Networks for Land Cover Classification using Multi-temporal Sentinel-2 Satellite Imagery
Mauro Martini
Vittorio Mazzia
Aleem Khaliq
Marcello Chiaberge
134
40
0
01 Apr 2021
A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net
A Deep Learning Approach to Mapping Irrigation: IrrMapper-U-Net
T. Colligan
David Ketchum
D. Brinkerhoff
M. Maneta
19
9
0
04 Mar 2021
Crop mapping from image time series: deep learning with multi-scale
  label hierarchies
Crop mapping from image time series: deep learning with multi-scale label hierarchies
Mehmet Özgür Türkoglu
Stefano Dáronco
Gregor Perich
F. Liebisch
Constantin Streit
Konrad Schindler
Jan Dirk Wegner
145
134
0
17 Feb 2021
Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using
  Machine Learning Methods Trained with Radiative Transfer Simulations
Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations
Aleksandra Wolanin
Gustau Camps-Valls
L. Gómez-Chova
Gonzalo Mateo-García
C. Tol
Yongguang Zhang
L. Guanter
65
148
0
07 Dec 2020
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data
  Using Deep Transfer Learning
Simultaneous Corn and Soybean Yield Prediction from Remote Sensing Data Using Deep Transfer Learning
S. Khaki
Hieu H. Pham
Lizhi Wang
44
124
0
05 Dec 2020
Crop Classification under Varying Cloud Cover with Neural Ordinary
  Differential Equations
Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations
Nando Metzger
Mehmet Özgür Türkoglu
Stefano Dáronco
Jan Dirk Wegner
Konrad Schindler
BDLAI4TS
99
30
0
04 Dec 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at
  Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Alexey Dosovitskiy
Lucas Beyer
Alexander Kolesnikov
Dirk Weissenborn
Xiaohua Zhai
...
Matthias Minderer
G. Heigold
Sylvain Gelly
Jakob Uszkoreit
N. Houlsby
ViT
723
41,796
0
22 Oct 2020
Are we done with ImageNet?
Are we done with ImageNet?
Lucas Beyer
Olivier J. Hénaff
Alexander Kolesnikov
Xiaohua Zhai
Aaron van den Oord
VLM
139
408
0
12 Jun 2020
Improvement in Land Cover and Crop Classification based on Temporal
  Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural
  Network (R-CNN)
Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
Vittorio Mazzia
Aleem Khaliq
Marcello Chiaberge
61
104
0
27 Apr 2020
Satellite Image Time Series Classification with Pixel-Set Encoders and
  Temporal Self-Attention
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention
Vivien Sainte Fare Garnot
Loic Landrieu
S. Giordano
N. Chehata
91
155
0
18 Nov 2019
Deep learning on edge: extracting field boundaries from satellite images
  with a convolutional neural network
Deep learning on edge: extracting field boundaries from satellite images with a convolutional neural network
F. Waldner
F. Diakogiannis
110
207
0
26 Oct 2019
Self-attention for raw optical Satellite Time Series Classification
Self-attention for raw optical Satellite Time Series Classification
M. Rußwurm
Marco Körner
AI4TS
88
267
0
23 Oct 2019
BreizhCrops: A Time Series Dataset for Crop Type Mapping
BreizhCrops: A Time Series Dataset for Crop Type Mapping
M. Rußwurm
Charlotte Pelletier
Maximilian Zollner
Sébastien Lefèvre
Marco Korner
AI4TS
106
76
0
28 May 2019
DuPLO: A DUal view Point deep Learning architecture for time series
  classificatiOn
DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn
R. Interdonato
Dino Ienco
R. Gaetano
K. Ose
AI4TS
60
142
0
20 Sep 2018
Multi-Temporal Land Cover Classification with Sequential Recurrent
  Encoders
Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders
M. Rußwurm
Marco Körner
124
278
0
06 Feb 2018
Rethinking Atrous Convolution for Semantic Image Segmentation
Rethinking Atrous Convolution for Semantic Image Segmentation
Liang-Chieh Chen
George Papandreou
Florian Schroff
Hartwig Adam
SSeg
234
8,515
0
17 Jun 2017
Attention Is All You Need
Attention Is All You Need
Ashish Vaswani
Noam M. Shazeer
Niki Parmar
Jakob Uszkoreit
Llion Jones
Aidan Gomez
Lukasz Kaiser
Illia Polosukhin
3DV
924
133,201
0
12 Jun 2017
Identity Mappings in Deep Residual Networks
Identity Mappings in Deep Residual Networks
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
381
10,217
0
16 Mar 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger
Philipp Fischer
Thomas Brox
SSeg3DV
1.9K
77,725
0
18 May 2015
Batch Normalization: Accelerating Deep Network Training by Reducing
  Internal Covariate Shift
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe
Christian Szegedy
OOD
533
43,394
0
11 Feb 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan
Andrew Zisserman
FAttMDE
1.8K
100,713
0
04 Sep 2014
Microsoft COCO: Common Objects in Context
Microsoft COCO: Common Objects in Context
Nayeon Lee
Michael Maire
Serge J. Belongie
Lubomir Bourdev
Ross B. Girshick
James Hays
Pietro Perona
Deva Ramanan
C. L. Zitnick
Piotr Dollár
ObjD
471
43,961
0
01 May 2014
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
497
7,675
0
03 Jul 2012
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