Agricultural Field Extraction

Chapter Contents (Back)
Agricultural Field. Field Extraction. Region-Based.
See also Irrigation Monitoring, Irrigated Field Detection, Land Use Analysis.
See also Rice Crop Analysis, Production, Detection, Health, Change.
See also Smallholder Analysis.

Yan, F.Q.[Feng-Qin], Yu, L.X.[Ling-Xue], Yang, C.B.[Chao-Bin], Zhang, S.W.[Shu-Wen],
Paddy Field Expansion and Aggregation Since the Mid-1950s in a Cold Region and Its Possible Causes,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Wagner, M.P.[Matthias P.], Oppelt, N.[Natascha],
Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction,
RS(12), No. 12, 2020, pp. xx-yy.
DOI Link 2006

Vlachopoulos, O.[Odysseas], Leblon, B.[Brigitte], Wang, J.F.[Jin-Fei], Haddadi, A.[Ataollah], LaRocque, A.[Armand], Patterson, G.[Greg],
Delineation of Crop Field Areas and Boundaries from UAS Imagery Using PBIA and GEOBIA with Random Forest Classification,
RS(12), No. 16, 2020, pp. xx-yy.
DOI Link 2008

Liu, J.[Jin], Zheng, H.[Haokun],
EFN: Field-Based Object Detection for Aerial Images,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

Chang, L.[Lena], Chen, Y.T.[Yi-Ting], Wang, J.H.[Jung-Hua], Chang, Y.L.[Yang-Lang],
Rice-Field Mapping with Sentinel-1A SAR Time-Series Data,
RS(13), No. 1, 2021, pp. xx-yy.
DOI Link 2101

Gilcher, M.[Mario], Udelhoven, T.[Thomas],
Field Geometry and the Spatial and Temporal Generalization of Crop Classification Algorithms: A Randomized Approach to Compare Pixel Based and Convolution Based Methods,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

Taravat, A.[Alireza], Wagner, M.P.[Matthias P.], Bonifacio, R.[Rogerio], Petit, D.[David],
Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

Waldner, F.[François], Diakogiannis, F.I.[Foivos I.], Batchelor, K.[Kathryn], Ciccotosto-Camp, M.[Michael], Cooper-Williams, E.[Elizabeth], Herrmann, C.[Chris], Mata, G.[Gonzalo], Toovey, A.[Andrew],
Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Wen, C.Y.[Cai-Yun], Lu, M.[Miao], Bi, Y.[Ying], Zhang, S.N.[Sheng-Nan], Xue, B.[Bing], Zhang, M.J.[Meng-Jie], Zhou, Q.B.[Qing-Bo], Wu, W.B.[Wen-Bin],
An Object-Based Genetic Programming Approach for Cropland Field Extraction,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203

See also New Genetic Programming-Based Approach to Object Detection in Mussel Farm Images, A. BibRef

Li, T.[Ting], Johansen, K.[Kasper], McCabe, M.F.[Matthew F.],
A machine learning approach for identifying and delineating agricultural fields and their multi-temporal dynamics using three decades of Landsat data,
PandRS(186), 2022, pp. 83-101.
Elsevier DOI 2203
Center-pivot field, Delineation, DBSCAN, Convolution neural networks, Spectral clustering, Random forest BibRef

Lu, R.[Rui], Wang, N.[Nan], Zhang, Y.B.[Yan-Bin], Lin, Y.N.[Ye-Neng], Wu, W.Q.[Wen-Qiang], Shi, Z.[Zhou],
Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-scale Feature Fusion in South Xinjiang, China,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Zhang, Z.Q.[Zhi-Qi], Lu, W.[Wen], Cao, J.S.[Jin-Shan], Xie, G.Q.[Guang-Qi],
MKANet: An Efficient Network with Sobel Boundary Loss for Land-Cover Classification of Satellite Remote Sensing Imagery,
RS(14), No. 18, 2022, pp. xx-yy.
DOI Link 2209

Ge, J.[Ji], Zhang, H.[Hong], Xu, L.[Lu], Sun, C.L.[Chun-Ling], Duan, H.X.[Hao-Xuan], Guo, Z.H.[Zi-Huan], Wang, C.[Chao],
A Physically Interpretable Rice Field Extraction Model for PolSAR Imagery,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Liu, X.C.[Xiang-Chen], Shao, Y.[Yun], Li, K.[Kun], Liu, Z.[Zhiqu], Liu, L.[Long], Xiao, X.[Xiulai],
Backscattering Statistics of Indoor Full-Polarization Scatterometric and Synthetic Aperture Radar Measurements of a Rice Field,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Xu, Y.[Yang], Xue, X.Y.[Xin-Yu], Sun, Z.[Zhu], Gu, W.[Wei], Cui, L.F.[Long-Fei], Jin, Y.[Yongkui], Lan, Y.[Yubin],
Deriving Agricultural Field Boundaries for Crop Management from Satellite Images Using Semantic Feature Pyramid Network,
RS(15), No. 11, 2023, pp. 2937.
DOI Link 2306

Li, M.M.[Meng-Meng], Long, J.[Jiang], Stein, A.[Alfred], Wang, X.Q.[Xiao-Qin],
Using a semantic edge-aware multi-task neural network to delineate agricultural parcels from remote sensing images,
PandRS(200), 2023, pp. 24-40.
Elsevier DOI 2306
Agricultural parcel delineation, SEANet, Multi-task neural networks, Semantic edge-aware detection, Uncertainty weighted loss BibRef

Chen, L.[Long], Song, W.L.[Wen-Long], Sun, T.[Tao], Lu, Y.Z.[Yi-Zhu], Jiang, W.[Wei], Liu, J.[Jun], Liu, H.J.[Hong-Jie], Feng, T.S.[Tian-Shi], Gui, R.J.[Rong-Jie], Abbas, H.[Haider], Meng, L.W.[Ling-Wei], Lin, S.J.[Sheng-Jie], He, Q.[Qian],
Field Patch Extraction Based on High-Resolution Imaging and U2-Net++ Convolutional Neural Networks,
RS(15), No. 20, 2023, pp. 4900.
DOI Link 2310

Awad, B.[Bahaa], Erer, I.[Isin],
FAUNet: Frequency Attention U-Net for Parcel Boundary Delineation in Satellite Images,
RS(15), No. 21, 2023, pp. 5123.
DOI Link 2311

Cai, Z.W.[Zhi-Wen], Hu, Q.[Qiong], Zhang, X.Y.[Xin-Yu], Yang, J.Y.[Jing-Ya], Wei, H.D.[Hao-Dong], Wang, J.[Jiayue], Zeng, Y.[Yelu], Yin, G.F.[Gao-Fei], Li, W.J.[Wen-Juan], You, L.Z.[Liang-Zhi], Xu, B.D.[Bao-Dong], Shi, Z.H.[Zhi-Hua],
Improving agricultural field parcel delineation with a dual branch spatiotemporal fusion network by integrating multimodal satellite data,
PandRS(205), 2023, pp. 34-49.
Elsevier DOI 2311
Agricultural field parcel delineation, Deep learning, Multimodal satellite data, Spatiotemporal fusion, Spatial transferability BibRef

Liu, L.[Lei], Li, G.[Guorun], Du, Y.F.[Yue-Feng], Li, X.Y.[Xiao-Yu], Wu, X.[Xiuheng], Qiao, Z.[Zhi], Wang, T.Y.[Tian-Yi],
CS-net: Conv-simpleformer network for agricultural image segmentation,
PR(147), 2024, pp. 110140.
Elsevier DOI 2312
Semantic segmentation, CS-net, Agricultural image, CNNs, Transformers, Simple-attention BibRef

Qi, L.[Liang], Zuo, D.F.[Dan-Feng], Wang, Y.R.[Yi-Rong], Tao, Y.[Ye], Tang, R.[Runkang], Shi, J.[Jiayu], Gong, J.J.[Jia-Jun], Li, B.[Bangyu],
Convolutional Neural Network-Based Method for Agriculture Plot Segmentation in Remote Sensing Images,
RS(16), No. 2, 2024, pp. 346.
DOI Link 2402

Nair, S.[Shruti], Sharifzadeh, S.[Sara], Palade, V.[Vasile],
Farmland Segmentation in Landsat 8 Satellite Images Using Deep Learning and Conditional Generative Adversarial Networks,
RS(16), No. 5, 2024, pp. 823.
DOI Link 2403

Chen, G.[Gang], Hammelman, C.[Colleen], Anantsuksomsri, S.[Sutee], Tontisirin, N.[Nij], Todd, A.R.[Amelia R.], Hicks, W.W.[William W.], Robinson, H.M.[Harris M.], Calloway, M.G.[Miles G.], Bell, G.M.[Grace M.], Kinsey, J.E.[John E.],
Fine-Scale (10 m) Dynamics of Smallholder Farming through COVID-19 in Eastern Thailand,
RS(16), No. 6, 2024, pp. 1035.
DOI Link 2403

Gundermann, N.[Niels], Lowe, W.[Welf], Fransson, J.E.S.[Johan E. S.], Olofsson, E.[Erika], Wehrenpfennig, A.[Andreas],
Object Identification in Land Parcels Using a Machine Learning Approach,
RS(16), No. 7, 2024, pp. 1143.
DOI Link 2404

Seedz, R.F.R.[Rodrigo Fill Rangel], Gaivota, V.N.L.[Vítor Nascimento Lourenço], Seedz, L.V.O.[Lucas Volochen Oldoni], Seedz, A.F.C.B.[Ana Flavia Carrara Bonamigo], Gaivota, W.S.[Wallas Santos], Seedz, B.S.O.[Bruno Silva Oliveira], Seedz, M.N.B.[Mateus Neves Barreto],
A Unified Framework for Cropland Field Boundary Detection and Segmentation,
Measurement, Image segmentation, Filtering, Pipelines, Neural networks BibRef

Meyer, L., Lemarchand, F., Sidiropoulos, P.,
A Deep Learning Architecture for Batch-mode Fully Automated Field Boundary Detection,
DOI Link 2012

Wakabayashi, H., Motohashi, K., Kitagami, T., Tjahjono, B., Dewayani, S., Hidayat, D., Hongo, C.,
Flooded Area Extraction of Rice Paddy Field in Indonesia Using Sentinel-1 Sar Data,
DOI Link 1904

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Smallholder Analysis .

Last update:May 6, 2024 at 15:50:14