Pixel to Parcel: Transformative Applications of Image Segmentation in Geospatial and Crop Research

Authors

  • Hui Zeng

    School of AI Engineering, Guangzhou College of Technology and Business, Guangzhou 510850, China

DOI:

https://doi.org/10.30564/jees.v8i3.13236
Received: 2 January 2026 | Revised: 25 February 2026 | Accepted: 28 February 2026 | Published Online: 17 March 2026

Abstract

The rising need for precision farming and sustainable land management has catalyzed the requirement for sophisticated means of deriving practical data from remote sensing images. Image segmentation, or the process of dividing the image into semantically relevant parts, has become a groundbreaking technology that allows resolving the problem of transitioning the pixel-level data to a parcel-level analysis. This review is a synthesis of the segmentation methods and their use in crop research and geospatial science. The architectures of pixel-based, object-based, and deep learning (convolutional neural networks, U-Net, Mask R-CNN, and Transformer models) are considered in terms of principles, capabilities, and limitations. Multi-spectral, hyperspectral, LiDAR, and SAR data are integrated to improve the efficiency of segmentation, allowing the possible delineation of fields, the classification of crops, health monitoring, monitoring of yields, and stress identification. In addition to agriculture, segmentation helps in land use and land cover mapping, identification of temporal change, monitoring of the environment, and is used in combination with GIS-based spatial modeling. Nevertheless, issues related to data heterogeneity, mixed pixels, computational requirements, and inadequate availability of labelled data still exist despite the major progress. The future directions involve multi-source data fusion, pixel-to-parcel pipeline automation, and predictive models based on AI, which are used to enhance its scalability, robustness, and the ability to monitor in real-time. This review makes it clear that the use of image segmentation as a tool in generating precision agriculture, sustainable land use, and informed geospatial.

Keywords:

Image Segmentation; Precision Agriculture; Geospatial Analysis; Crop Monitoring; Remote Sensing

References

[1] Lu, B., Dao, P., Liu, J., et al., 2020. Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture. Remote Sensing. 12(16), 2659. DOI: https://doi.org/10.3390/rs12162659

[2] Meena, T., Singh, V.K., Roy, S., 2024. Defining Problems and Identifying Opportunities in Agriculture and Natural Resources. In: Raval, M.S., Chaudhary, S., Adinarayana, J., et al. (Eds.). Harnessing Data Science for Sustainable Agriculture and Natural Resource Management, Studies in Big Data. Springer Nature: Singapore. pp. 25–45. DOI: https://doi.org/10.1007/978-981-97-7762-4_2

[3] Phang, S.K., Chiang, T.H.A., Happonen, A., et al., 2023. From Satellite to UAV-Based Remote Sensing: A Review on Precision Agriculture. IEEE Access. 11, 127057–127076. DOI: https://doi.org/10.1109/ACCESS.2023.3330886

[4] Khanal, S., Kc, K., Fulton, J.P., et al., 2020. Remote Sensing in Agriculture—Accomplishments, Limitations, and Opportunities. Remote Sensing. 12(22), 3783. DOI: https://doi.org/10.3390/rs12223783

[5] Luo, Z., Yang, W., Yuan, Y., et al., 2024. Semantic segmentation of agricultural images: A survey. Information Processing in Agriculture. 11(2), 172–186. DOI: https://doi.org/10.1016/j.inpa.2023.02.001

[6] Hossain, M.D., Chen, D., 2024. Remote sensing image segmentation: Methods, approaches, and advances. In Remote Sensing Handbook. CRC Press: Boca Raton, FL, USA. pp. 117–144.

[7] Bhatti, U.A., Tang, H., Wu, G., et al., 2023. Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence. International Journal of Intelligent Systems. 2023(1), 8342104. DOI: https://doi.org/10.1155/2023/8342104

[8] Chouhan, S.S., Kaul, A., Singh, U.P., 2019. Image Segmentation Using Computational Intelligence Techniques: Review. Archives of Computational Methods in Engineering. 26(3), 533–596. DOI: https://doi.org/10.1007/s11831-018-9257-4

[9] Wang, M., Wang, J., Cui, Y., et al., 2022. Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy. Agronomy. 12(10), 2342. DOI: https://doi.org/10.3390/agronomy12102342

[10] Patel, B., Sharaff, A., 2023. Rice variety classification & yield prediction using semantic segmentation of agro-morphological characteristics. Multimedia Tools and Applications. 82(29), 45567–45584. DOI: https://doi.org/10.1007/s11042-023-15549-w

[11] Mas, J.-F., González, R., 2015. Change Detection and Land Use/Land Cover Database Updating Using Image Segmentation, Gis Analysis and Visual Interpretation. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XL-3/W3, 61–65. DOI: https://doi.org/10.5194/isprsarchives-XL-3-W3-61-2015

[12] Wu, B., Zhang, M., Zeng, H., et al., 2023. Challenges and opportunities in remote sensing-based crop monitoring: A review. National Science Review. 10(4), nwac290. DOI: https://doi.org/10.1093/nsr/nwac290

[13] Sun, C., Bian, Y., Zhou, T., et al., 2019. Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region. Sensors. 19(10), 2401. DOI: https://doi.org/10.3390/s19102401

[14] Zheng, J., Ye, Z., Wen, Y., et al., 2026. A Comprehensive Review of Agricultural Parcel and Boundary Delineation From Remote Sensing Images: Recent progress and future perspectives. IEEE Geoscience and Remote Sensing Magazine. 2–33. DOI: https://doi.org/10.1109/MGRS.2026.3658493

[15] Sabir, R.M., Mehmood, K., Sarwar, A., et al., 2024. Remote Sensing and Precision Agriculture: A Sustainable Future. In: Kanga, S., Singh, S.K., Shevkani, K., et al. (Eds.). Transforming Agricultural Management for a Sustainable Future, World Sustainability Series. Springer Nature: Cham, Switzerland. pp. 75–103. DOI: https://doi.org/10.1007/978-3-031-63430-7_4

[16] Blaschke, T., Burnett, C., Pekkarinen, A., 2004. Image Segmentation Methods for Object-based Analysis and Classification. In: Jong, S.M.D., Meer, F.D.V.D. (Eds.). Remote Sensing Image Analysis: Including the Spatial Domain, Remote Sensing and Digital Image Processing. Springer: Dordrecht, The Netherlands. pp. 211–236. DOI: https://doi.org/10.1007/978-1-4020-2560-0_12

[17] Reddy, G.S., Anu, M., Chaitanya, K., 2024. Spatial Analysis and Geospatial Data in Agriculture. In Beyond the Plow: A Guide to the Latest Trends in Modern Agriculture. AkiNik Publications: New Delhi, India. p. 93.

[18] Zhao, J., Zhong, Y., Hu, X., et al., 2020. A robust spectral-spatial approach to identifying heterogeneous crops using remote sensing imagery with high spectral and spatial resolutions. Remote Sensing of Environment. 239, 111605. DOI: https://doi.org/10.1016/j.rse.2019.111605

[19] Hussain, M., Chen, D., Cheng, A., et al., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing. 80, 91–106. DOI: https://doi.org/10.1016/j.isprsjprs.2013.03.006

[20] García Pedrero, Á.M., 2016. Spatio-temporal Analysis of Agricultural Landscape Images: A Superpixel-based Approach [PhD Thesis]. Universidad Politécnica de Madrid: Madrid, Spain. DOI: https://doi.org/10.20868/UPM.thesis.44562

[21] Goodin, D.G., Anibas, K.L., Bezymennyi, M., 2015. Mapping land cover and land use from object-based classification: An example from a complex agricultural landscape. International Journal of Remote Sensing. 36(18), 4702–4723. DOI: https://doi.org/10.1080/01431161.2015.1088674

[22] Minaee, S., Boykov, Y.Y., Porikli, F., et al., 2021. Image Segmentation Using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(7), 3523–3542. DOI: https://doi.org/10.1109/TPAMI.2021.3059968

[23] Liu, X., Deng, Z., Yang, Y., 2019. Recent progress in semantic image segmentation. Artificial Intelligence Review. 52(2), 1089–1106. DOI: https://doi.org/10.1007/s10462-018-9641-3

[24] Syed Zaini, S.Z., Sofia, N.N., Marzuki, M., et al., 2019. Image Quality Assessment for Image Segmentation Algorithms: Qualitative and Quantitative Analyses. In Proceedings of the 2019 9th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia, 1 November 2019; pp. 66–71. DOI: https://doi.org/10.1109/ICCSCE47578.2019.9068561

[25] Wang, Z., Wang, E., Zhu, Y., 2020. Image segmentation evaluation: A survey of methods. Artificial Intelligence Review. 53(8), 5637–5674. DOI: https://doi.org/10.1007/s10462-020-09830-9

[26] Nagavi, J.C., Shukla, B.K., Bhati, A., et al., 2024. Harnessing Geospatial Technology for Sustainable Development: A Multifaceted Analysis of Current Practices and Future Prospects. In: Sharma, C., Shukla, A.K., Pathak, S., et al. (Eds.). Sustainable Development and Geospatial Technology. Springer Nature: Cham, Switzerland. pp. 147–170. DOI: https://doi.org/10.1007/978-3-031-65683-5_8

[27] Zheng, B., Liang, B., Liang, S., et al., 2026. Temporal Data Explainability in Remote Sensing for Agriculture: A Systematic Review. SSRN Journal. DOI: https://doi.org/10.2139/ssrn.6105227

[28] Nakalembe, C., Becker-Reshef, I., Bonifacio, R., et al., 2021. A review of satellite-based global agricultural monitoring systems available for Africa. Global Food Security. 29, 100543. DOI: https://doi.org/10.1016/j.gfs.2021.100543

[29] Olson, D., Anderson, J., 2021. Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture. Agronomy Journal. 113(2), 971–992. DOI: https://doi.org/10.1002/agj2.20595

[30] Zhao, W., Wang, M., Pham, V.T., 2023. Unmanned Aerial Vehicle and Geospatial Analysis in Smart Irrigation and Crop Monitoring on IoT Platform. Mobile Information Systems. 2023, 1–12. DOI: https://doi.org/10.1155/2023/4213645

[31] Haque, M.A., Reza, M.N., Ali, M., et al., 2024. Effects of environmental conditions on vegetation indices from multispectral images: A review. Korean Journal of Remote Sensing. 40(4), 319–341.

[32] Thenkabail, P.S., Lyon, J.G., Huete, A., 2018. Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops. In: Thenkabail, P.S., Lyon, J.G., Huete, A. (Eds.). Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation. CRC Press: Boca Raton, FL, USA. pp. 3–37. DOI: https://doi.org/10.1201/9781315164151-1

[33] Omasa, K., Hosoi, F., Konishi, A., 2006. 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany. 58(4), 881–898. DOI: https://doi.org/10.1093/jxb/erl142

[34] Meng, L., Yan, C., Lv, S., et al., 2024. Synthetic Aperture Radar for Geosciences. Reviews of Geophysics. 62(3), e2023RG000821. DOI: https://doi.org/10.1029/2023RG000821

[35] Bakx, J.P.G., Janssen, L., Schetselaar, E.M., et al., 2012. The Core of GISience: A Processed-Based Approach. University of Twente: Enschede, The Netherlands.

[36] Panigrahi, N., Mohan, B., Athithan, G., 2011. Pre-Processing Algorithm for Rectification of Geometric Distortions in Satellite Images. Defence Science Journal. 61(2), 174–179. DOI: https://doi.org/10.14429/dsj.61.421

[37] Ozdogan, M., Yang, Y., Allez, G., et al., 2010. Remote Sensing of Irrigated Agriculture: Opportunities and Challenges. Remote Sensing. 2(9), 2274–2304. DOI: https://doi.org/10.3390/rs2092274

[38] McCabe, M.F., Houborg, R., Lucieer, A., 2016. High-resolution sensing for precision agriculture: From Earth-observing satellites to unmanned aerial vehicles. In Proceedings of the 2016 SPIE Remote Sensing, Edinburgh, UK, 25 October 2016; p. 999811. DOI: https://doi.org/10.1117/12.2241289

[39] Adão, T., Hruška, J., Pádua, L., et al., 2017. Hyperspectral Imaging: A Review on UAV-Based Sensors, Data Processing and Applications for Agriculture and Forestry. Remote Sensing. 9(11), 1110. DOI: https://doi.org/10.3390/rs9111110

[40] Vibhute, A., Bodhe, S., 2012. Applications of Image Processing in Agriculture: A Survey. International Journal of Computer Applications. 52(2), 34–40. DOI: https://doi.org/10.5120/8176-1495

[41] Mancini, A., Frontoni, E., Zingaretti, P., 2019. Satellite and UAV data for Precision Agriculture Applications. In Proceedings of the 2019 International Conference on Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 11–14 June 2019; pp. 491–497. DOI: https://doi.org/10.1109/ICUAS.2019.8797930

[42] Jadhav, J.K., Singh, R.P., 2018. Automatic semantic segmentation and classification of remote sensing data for agriculture. Mathematical Models in Engineering. 4(2), 112–137. DOI: https://doi.org/10.21595/mme.2018.19840

[43] Kumar, K.V., Jayasankar, T., 2019. An identification of crop disease using image segmentation. International Journal of Pharmaceutical Sciences and Research. 10(3), 1054–1064.

[44] Payne, A.B., Walsh, K.B., Subedi, P.P., et al., 2013. Estimation of mango crop yield using image analysis – Segmentation method. Computers and Electronics in Agriculture. 91, 57–64. DOI: https://doi.org/10.1016/j.compag.2012.11.009

[45] Bargoti, S., Underwood, J.P., 2017. Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards. Journal of Field Robotics. 34(6), 1039–1060. DOI: https://doi.org/10.1002/rob.21699

[46] Anand, S., Sandhu, S.K., 2024. Biotic Stress Management in Field Crops Using Artificial Intelligence Technologies. In: Pandey, K., Kushwaha, N.L., Pande, C.B., et al. (Eds.). Artificial Intelligence and Smart Agriculture, Advances in Geographical and Environmental Sciences. Springer Nature: Singapore. pp. 315–335. DOI: https://doi.org/10.1007/978-981-97-0341-8_16

[47] Prabhakar, M., Prasad, Y.G., Rao, M.N., 2012. Remote Sensing of Biotic Stress in Crop Plants and Its Applications for Pest Management. In: Venkateswarlu, B., Shanker, A.K., Shanker, C., et al. (Eds.). Crop Stress and Its Management: Perspectives and Strategies. Springer: Dordrecht, The Netherlands. pp. 517–545. DOI: https://doi.org/10.1007/978-94-007-2220-0_16

[48] Devereux, B.J., Amable, G.S., Posada, C.C., 2004. An efficient image segmentation algorithm for landscape analysis. International Journal of Applied Earth Observation and Geoinformation. 6(1), 47–61. DOI: https://doi.org/10.1016/j.jag.2004.07.007

[49] Xu, Z., Su, C., Zhang, X., 2021. A semantic segmentation method with category boundary for Land Use and Land Cover (LULC) mapping of Very-High Resolution (VHR) remote sensing image. International Journal of Remote Sensing. 42(8), 3146–3165. DOI: https://doi.org/10.1080/01431161.2020.1871100

[50] Padhiary, M., Saikia, P., Roy, P., et al., 2025. A Review on Advancing Agricultural Efficiency through Geographic Information Systems, Remote Sensing, and Automated Systems. Cureus Journal of Engineering. DOI: https://doi.org/10.7759/s44388-024-00559-7

[51] Marcello, C., Khaliq, A., 2020. Advancements in Multi-Temporal Remote Sensing Data Analysis Techniques for Precision Agriculture. Polytechnic University of Turin: Turin, Italy.

[52] Rossi, C., McMillan, N.A., Schweizer, J.M., et al., 2024. Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity. Environmental Research Letters. 19(7), 074023. DOI: https://doi.org/10.1088/1748-9326/ad545a

[53] Blaschke, T., Lang, S., Lorup, E., et al., 2000. Object-oriented image processing in an integrated GIS/remote sensing environment and perspectives for environmental applications. Environmental Information for Planning, Politics and the Public. 2(1995), 555–570.

[54] Deur, M., Gašparović, M., Balenović, I., 2021. An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery. Remote Sensing. 13(10), 1868. DOI: https://doi.org/10.3390/rs13101868

[55] Chen, C., Zhang, P., Zhang, H., et al., 2020. Deep Learning on Computational-Resource-Limited Platforms: A Survey. Mobile Information Systems. 2020, 1–19. DOI: https://doi.org/10.1155/2020/8454327

[56] Johnson, B.A., Ma, L., 2020. Image Segmentation and Object-Based Image Analysis for Environmental Monitoring: Recent Areas of Interest, Researchers’ Views on the Future Priorities. Remote Sensing. 12(11), 1772. DOI: https://doi.org/10.3390/rs12111772

Downloads

How to Cite

Zeng, H. (2026). Pixel to Parcel: Transformative Applications of Image Segmentation in Geospatial and Crop Research. Journal of Environmental & Earth Sciences, 8(3), 112–125. https://doi.org/10.30564/jees.v8i3.13236

Issue

Vol. 8 , Iss. 3 (March 2026)

Article Type

Review

License

Copyright © 2026 Hui Zeng

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC