Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-1829-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-1829-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 May 2021
Data description paper |
| 03 May 2021
| 03 May 2021
A new satellite-derived dataset for marine aquaculture areas in China's coastal region
Yongyong Fu
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Jinsong Deng
CORRESPONDING AUTHOR
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Hongquan Wang
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Alexis Comber
School of Geography, University of Leeds, Leeds LS1 9JT, UK
Wu Yang
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Wenqiang Wu
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Shixue You
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Yi Lin
Department of Geography, University of Hong Kong, Hong Kong SAR
999077, China
Ke Wang
College of Environmental and Resource Sciences, Zhejiang University,
Hangzhou, 310058, China
Related authors
No articles found.
Alexis Comber, Molly Asher, Yiyu Wang, Minh Kieu, Quang-Thanh Bui, Thi Thuy Hang Nguyen, Hoang Huu Phe, and Nick Malleson
AGILE GIScience Ser., 6, 18, https://doi.org/10.5194/agile-giss-6-18-2025, https://doi.org/10.5194/agile-giss-6-18-2025, 2025
Alexis Comber, Paul Harris, and Chris Brunsdon
AGILE GIScience Ser., 6, 19, https://doi.org/10.5194/agile-giss-6-19-2025, https://doi.org/10.5194/agile-giss-6-19-2025, 2025
Alexis Comber, Minh Kieu, Quang-Thanh Bui, and Nick Malleson
AGILE GIScience Ser., 5, 20, https://doi.org/10.5194/agile-giss-5-20-2024, https://doi.org/10.5194/agile-giss-5-20-2024, 2024
Alexis Comber, Eleftherios Zormpas, Rachel Queen, and Simon J. Cockell
AGILE GIScience Ser., 5, 21, https://doi.org/10.5194/agile-giss-5-21-2024, https://doi.org/10.5194/agile-giss-5-21-2024, 2024
Nan Cui, Nick Malleson, Vikki Houlden, and Alexis Comber
AGILE GIScience Ser., 4, 22, https://doi.org/10.5194/agile-giss-4-22-2023, https://doi.org/10.5194/agile-giss-4-22-2023, 2023
Yiyu Wang, Jiaqi Ge, and Alexis Comber
AGILE GIScience Ser., 4, 50, https://doi.org/10.5194/agile-giss-4-50-2023, https://doi.org/10.5194/agile-giss-4-50-2023, 2023
Yiyu Wang, Jiaqi Ge, and Alexis Comber
AGILE GIScience Ser., 3, 68, https://doi.org/10.5194/agile-giss-3-68-2022, https://doi.org/10.5194/agile-giss-3-68-2022, 2022
Alexis Comber, Paul Harris, Kristina Bratkova, Hoang Huu Phe, Minh Kieu, Quang Thanh Bui, Thi Thuy Hang Nguyen, Eric Wanjau, and Nick Malleson
AGILE GIScience Ser., 3, 30, https://doi.org/10.5194/agile-giss-3-30-2022, https://doi.org/10.5194/agile-giss-3-30-2022, 2022
Alexis Comber, Paul Harris, and Chris Brunsdon
AGILE GIScience Ser., 3, 31, https://doi.org/10.5194/agile-giss-3-31-2022, https://doi.org/10.5194/agile-giss-3-31-2022, 2022
Yiyu Wang, Jiaqi Ge, and Alexis Comber
AGILE GIScience Ser., 2, 42, https://doi.org/10.5194/agile-giss-2-42-2021, https://doi.org/10.5194/agile-giss-2-42-2021, 2021
Cited articles
Badrinarayanan, V., Kendall, A., and Cipolla, R.: SegNet: A Deep
Convolutional Encoder-Decoder Architecture for Image Segmentation, IEEE
Trans. Pattern Anal. Mach. Intell., 39, 2481–2495,
https://doi.org/10.1109/TPAMI.2016.2644615, 2017.
Bertasius, G., Shi, J., and Torresani, L.: Semantic Segmentation with
Boundary Neural Fields, in: 2016 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), IEEE, Las Vegas, NV, USA, 27–30 June 2016, 3602–3610, 2016.
Blaschke, T.: Object based image analysis for remote sensing, ISPRS J.
Photogramm. Remote Sens., 65, 2–16, https://doi.org/10.1016/j.isprsjprs.2009.06.004,
2010.
Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E.,
Queiroz Feitosa, R., van der Meer, F., van der Werff, H., van Coillie, F.,
and Tiede, D.: Geographic Object-Based Image Analysis - Towards a new
paradigm, ISPRS J. Photogramm. Remote Sens., 87, 180–191,
https://doi.org/10.1016/j.isprsjprs.2013.09.014, 2014.
Burbridge, P., Hendrick, V., Roth, E., and Rosenthal, H.: Social and economic policy issues
relevant to marine aquaculture, J. Appl. Ichthyol., 17, 194–206,
https://doi.org/10.1046/j.1439-0426.2001.00316.x, 2001.
Bureau of Fisheries of the Ministry of Agriculture: China Fishery
Statistical Yearbook 2001, China Agriculture Press, Beijing, China, 2001.
Bureau of Fisheries of the Ministry of Agriculture: China Fishery
Statistical Yearbook 2020, China Agriculture Press, Beijing, China, 2020.
Campbell, B. and Pauly, D.: Mariculture: A global analysis of production
trends since 1950, Mar. Policy, 39, 94–100,
https://doi.org/10.1016/j.marpol.2012.10.009, 2013.
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L.:
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous
Convolution, and Fully Connected CRFs, IEEE Trans. Pattern Anal. Mach.
Intell., 40, 834–848, https://doi.org/10.1109/TPAMI.2017.2699184, 2018.
Chuang, L., Ruixiang, S., Yinghua, Z., Yan, S., Junhua, M., Lizong, W.,
Wenbo, C., Doko, T., Lijun, C., Tingting, L., Zui, T., and Yunqiang, Z.:
Global Multiple Scale Shorelines Dataset Based on Google Earth Images
(2015), Digital Journal of Global Change Data Repository, https://doi.org/10.3974/geodb.2019.04.13.V1, 2019.
Cui, B., Fei, D., Shao, G., Lu, Y., and Chu, J.: Extracting Raft Aquaculture
Areas from Remote Sensing Images via an Improved U-Net with a PSE Structure,
Remote Sens., 11, 2053, https://doi.org/10.3390/rs11172053, 2019.
Duan, Y., Li, X., Zhang, L., Chen, D., Liu, S., and Ji, H.: Mapping
national-scale aquaculture ponds based on the Google Earth Engine in the
Chinese coastal zone, Aquaculture, 520, 734666,
https://doi.org/10.1016/j.aquaculture.2019.734666, 2020.
Efron, B. and Tibshirani, R.: Improvements on cross-validation: The .632+
bootstrap method, J. Am. Stat. Assoc., 92, 548–560,
https://doi.org/10.1080/01621459.1997.10474007, 1997.
Eigen, D. and Fergus, R.: Predicting depth, surface normals and semantic
labels with a common multi-scale convolutional architecture, in: 2015 IEEE
International Conference on Computer Vision (ICCV), IEEE,
Santiago, Chile, 7–13 December 2015, 2650–2658, 2015.
Fan, J., Chu, J., Geng, J., and Zhang, F.: Floating raft aquaculture
information automatic extraction based on high resolution SAR images, in:
2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, Milan, Italy, 26–31 July 2015, 3898–3901, 2015.
Fu, Y., Deng, J., Ye, Z., Gan, M., Wang, K., Wu, J., Yang, W., and Xiao, G.:
Coastal aquaculture mapping from very high spatial resolution imagery by
combining object-based neighbor features, Sustainability, 11, 637, https://doi.org/10.3390/su11030637,
2019a.
Fu, Y., Ye, Z., Deng, J., Zheng, X., Huang, Y., Yang, W., Wang, Y., and Wang,
K.: Finer Resolution Mapping of Marine Aquaculture Areas Using WorldView-2
Imagery and a Hierarchical Cascade Convolutional Neural Network, Remote
Sens., 11, 1678, https://doi.org/10.3390/rs11141678, 2019b.
Fu, Y., Liu, K., Shen, Z., Deng, J., Gan, M., Liu, X., Lu, D., and Wang, K.:
Mapping Impervious Surfaces in Town–Rural Transition Belts Using China's
GF-2 Imagery and Object-Based Deep CNNs, Remote Sens., 11, 280,
https://doi.org/10.3390/rs11030280, 2019c.
Fu, Y., Deng, J., Wang H., Comber, A., Yang, W., Wu, W., You, X., Lin, Y., and
Wang, K.: A new satellite-derived dataset for marine aquaculture in the
China's coastal region, Data set, Zenodo,
https://doi.org/10.5281/zenodo.3833225, 2020.
Galil, B. S.: Taking stock: Inventory of alien species in the Mediterranean
sea, Biol. Invasions, 11, 359–372, https://doi.org/10.1007/s10530-008-9253-y, 2009.
Gentry, R. R., Froehlich, H. E., Grimm, D., Kareiva, P., Parke, M., Rust,
M., Gaines, S. D., and Halpern, B. S.: Mapping the global potential for
marine aquaculture, Nat. Ecol. Evol., 1, 1317–1324,
https://doi.org/10.1038/s41559-017-0257-9, 2017.
He, K., Zhang, X., Ren, S., and Sun, J.: Spatial Pyramid Pooling in Deep
Convolutional Networks for Visual Recognition, IEEE Trans. Pattern Anal.
Mach. Intell., 37, 1904–1916, https://doi.org/10.1109/TPAMI.2015.2389824, 2015.
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network
training by reducing internal covariate shift, in: Proceedings of the
International Conference on Machine Learning, Lille, France, 6–11 July 2015, 448–456,
2015.
Li, H., Kadav, A., Durdanovic, I., Samet, H., and Graf, H. P.: Pruning
Filters for Efficient ConvNets, in: International Conference on Learning
Representations, Toulon, France, 24–26 April 2017, 1–13, 2017.
Liang, Y., Cheng, X., Zhu, H., Shutes, B., Yan, B., Zhou, Q., and Yu, X.:
Historical Evolution of Mariculture in China During Past 40 Years and Its
Impacts on Eco-environment, Chinese Geogr. Sci., 28, 363–373,
https://doi.org/10.1007/s11769-018- 0940-z, 2018.
Lin, D., Dai, J., Jia, J., He, K., and Sun, J.: ScribbleSup:
Scribble-Supervised Convolutional Networks for Semantic Segmentation, in
2016 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Las Vegas, NV, USA, 27–30 June 2016,
3159–3167, 2016.
Liu, Y., Zhong, Y., Fei, F., and Zhang, L.: Scene semantic classification
based on random-scale stretched convolutional neural network for
high-spatial resolution remote sensing imagery, in: 2016 IEEE International
Geoscience and Remote Sensing Symposium (IGARSS), IEEE,
Beijing, China, 763–766, 2016.
Liu, Y., Yu, J., and Han, Y.: Understanding the effective receptive field in
semantic image segmentation, Multimed. Tools Appl., 77, 22159–22171,
https://doi.org/10.1007/s11042-018-5704-3, 2018.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE
Computer Society Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015, 3431–3440, 2015.
Lu, Y., Li, Q., Du, X., Wang, H., and Liu, J.: A Method of Coastal
Aquaculture Area Automatic Extraction with High Spatial Resolution Images,
Remote Sens. Technol. Appl., 30, 486–494,
https://doi.org/10.11873/j.issn.1004-0323.2015.3.0486, 2015.
Lyons, M. B., Keith, D. A., Phinn, S. R., Mason, T. J., and Elith, J.: A
comparison of resampling methods for remote sensing classification and
accuracy assessment, Remote Sens. Environ., 208, 145–153,
https://doi.org/10.1016/j.rse.2018.02.026, 2018.
Marmanis, D., Schindler, K., Wegner, J. D., Galliani, S., Datcu, M., and
Stilla, U.: Classification with an edge: Improving semantic image
segmentation with boundary detection, ISPRS J. Photogramm. Remote Sens.,
135, 158–172, https://doi.org/10.1016/j.isprsjprs.2017.11.009, 2018.
Noh, H., Hong, S., and Han, B.: Learning deconvolution network for semantic
segmentation, in: 2015 IEEE International Conference on Computer Vision
(ICCV), IEEE, Santiago, Chile, 7–13 December 2015, 1520–1528, 2015.
Padilla, M., Stehman, S. V., and Chuvieco, E.: Validation of the 2008
MODIS-MCD45 global burned area product using stratified random sampling,
Remote Sens. Environ., 144, 187–196, https://doi.org/10.1016/j.rse.2014.01.008, 2014.
Pathak, D., Krahenbuhl, P., and Darrell, T.: Constrained convolutional neural
networks for weakly supervised segmentation, in: 2015 IEEE International
Conference on Computer Vision, IEEE, Santiago, Chile, 7–13 December 2015, 1796–1804, 2015.
Pinheiro, P. O., Lin, T. Y., Collobert, R., and Dollár, P.: Learning to
refine object segments, in: European conference on computer vision, Springer Nature, Amsterdam, The Netherlands, 8–16 October 2016, 75–91, 2016.
Porrello, S., Tomassetti, P., Manzueto, L., Finoia, M. G., Persia, E.,
Mercatali, I., and Stipa, P.: The influence of marine cages on the sediment
chemistry in the Western Mediterranean Sea, Aquaculture, 249,
145–158, https://doi.org/10.1016/j.aquaculture.2005.02.042, 2005.
POWERS, D. M. W.: Evaluation: From precision, recall and f-measure to roc,
informedness, markedness and correlation, J. Mach. Learn. Technol., 2,
37–63, 2011.
Ramezan, C. A., Warner, T. A., and Maxwell, A. E.: Evaluation of sampling and
cross-validation tuning strategies for regional-scale machine learning
classification, Remote Sens., 11, 185, https://doi.org/10.3390/rs11020185, 2019.
Rigos, G. and Katharios, P.: Pathological obstacles of newly-introduced fish
species in Mediterranean mariculture: A review, Rev. Fish Biol. Fish.,
20, 47–70, https://doi.org/10.1007/s11160-009-9120-7, 2010.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, in: Proceedings of the Medical Image Computing
and Computer-Assisted Intervention (MICCAI 2015), Munich,
Germany, 5–9 October 2015, 234–241, 2015.
Rubio-Portillo, E., Villamor, A., Fernandez-Gonzalez, V., Antón, J., and
Sanchez -Jerez, P.: Exploring changes in bacterial communities to assess the
influence of fish farming on marine sediments, Aquaculture, 506, 459–464,
https://doi.org/10.1016/j.aquaculture.2019.03.051, 2019.
Shi, T., Xu, Q., Zou, Z., and Shi, Z.: Automatic Raft Labeling for Remote
Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network,
Remote Sens., 10, 1130, https://doi.org/10.3390/rs10071130, 2018.
Tovar, A., Moreno, C., Mánuel -Vez, M. P., and García -Vargas, M.:
Environmental impacts of intensive aquaculture in marine waters, Water Res.,
34, 334–342, https://doi.org/10.1016/S0043-1354(99)00102-5, 2000.
Wang, M., Cui, Q., Wang, J., Ming, D., and Lv, G.: Raft cultivation area
extraction from high resolution remote sensing imagery by fusing multi-scale
region-line primitive association features, ISPRS J. Photogramm. Remote
Sens., 123, 104–113, https://doi.org/10.1016/j.isprsjprs.2016.10.008, 2017.
Xiao, L., Haijun, H., Xiguang, Y., and Liwen, Y.: Method to extract
raft-cultivation area based on SPOT image, Sci. Surv. Mapp., 38, 41–43,
https://doi.org/10.16251/j.cnki.1009-2307.2013.02.033, 2013.
Yim, J., Joo, D., Bae, J., and Kim, J.: A gift from knowledge distillation:
Fast optimization, network minimization and transfer learning, in: 2017 IEEE
Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Honolulu, HI, USA, 21–26 July 2017, 1063–6919, 2017.
Zhang, L., Zhang, L., and Du, B.: Deep learning for remote sensing data: A
technical tutorial on the state of the art, IEEE Geosci. Remote Sens. Mag.,
4, 22–40, https://doi.org/10.1109/MGRS.2016.2540798, 2016.
Zhang, X., Zou, J., He, K., and Sun, J.: Accelerating Very Deep Convolutional
Networks for Classification and Detection, IEEE Trans. Pattern Anal. Mach.
Intell., 38, 1943–1955, https://doi.org/10.1109/TPAMI.2015.2502579, 2016.
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J.: Pyramid scene parsing
network, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), IEEE, Honolulu, HI, USA, 21–26 July 2017, 6230–6239, 2017.
Zhao, W. and Du, S.: Learning multiscale and deep representations for
classifying remotely sensed imagery, ISPRS J. Photogramm. Remote Sens., 113,
155–165, https://doi.org/10.1016/j.isprsjprs.2016.01.004, 2016.
Zheng, Y., Wu, J., Wang, A., and Chen, J.: Object-and pixel-based
classifications of macroalgae farming area with high spatial resolution
imagery, Geocarto Int., 33, 1048–1063, https://doi.org/10.1080/10106049.2017.1333531, 2017.
Short summary
Marine aquaculture areas in a region up to 30 km from the coast in China were mapped for the first time. It was found to cover a total area of ~1100 km2, of which more than 85 % is marine plant culture areas, with 87 % found in four coastal provinces. The results confirm the applicability and effectiveness of deep learning when applied to GF-1 data at the national scale, identifying the detailed spatial distributions and supporting the sustainable management of coastal resources in China.
Marine aquaculture areas in a region up to 30 km from the coast in China were mapped for the...
Altmetrics
Final-revised paper
Preprint