Journal of Geodesy and Geoinformation Science ›› 2022, Vol. 5 ›› Issue (4): 10-22.doi: 10.11947/j.JGGS.2022.0402

• Special Issue • Previous Articles     Next Articles

Automated Delineation of Smallholder Farm Fields Using Fully Convolutional Networks and Generative Adversarial Networks

Qiuyu YAN1(),Wufan ZHAO2(),Xiao HUANG3,Xianwei LYU4   

  1. 1. Nanjing R&D.C, PIESAT International Information Technology Limited, Nanjing 210012, China
    2. Department of Earth Observation Science, Faculty ITC, University of Twente, Enschede 7500AE, the Netherlands
    3. Department of Geosciences, University of Arkansas, Fayetteville, 72701. USA
    4. State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China
  • Received:2022-06-15 Accepted:2022-09-15 Online:2022-12-20 Published:2023-03-15
  • Contact: Wufan ZHAO
  • About author:Qiuyu YAN (1995—), male, master, majors in geoinformatics and earth observation, employed in PIESAT as an algorithm engineer. E-mail:
  • Supported by:
    Foundation of Anhui Province Key Laboratory of Physical Geographic Environment(2022PGE012)


Accurate boundaries of smallholder farm fields are important and indispensable geo-information that benefits farmers, managers, and policymakers in terms of better managing and utilizing their agricultural resources. Due to their small size, irregular shape, and the use of mixed-cropping techniques, the farm fields of smallholder can be difficult to delineate automatically. In recent years, numerous studies on field contour extraction using a deep Convolutional Neural Network (CNN) have been proposed. However, there is a relative shortage of labeled data for filed boundaries, thus affecting the training effect of CNN. Traditional methods mostly use image flipping, and random rotation for data augmentation. In this paper, we propose to apply Generative Adversarial Network (GAN) for the data augmentation of farm fields label to increase the diversity of samples. Specifically, we propose an automated method featured by Fully Convolutional Neural networks (FCN) in combination with GAN to improve the delineation accuracy of smallholder farms from Very High Resolution (VHR) images. We first investigate four State-Of-The-Art (SOTA) FCN architectures, i.e., U-Net, PSPNet, SegNet and OCRNet, to find the optimal architecture in the contour detection task of smallholder farm fields. Second, we apply the identified optimal FCN architecture in combination with Contour GAN and pixel2pixel GAN to improve the accuracy of contour detection. We test our method on the study area in the Sudano-Sahelian savanna region of northern Nigeria. The best combination achieved F1 scores of 0.686 on Test Set 1 (TS1), 0.684 on Test Set 2 (TS2), and 0.691 on Test Set 3 (TS3). Results indicate that our architecture adapts to a variety of advanced networks and proves its effectiveness in this task. The conceptual, theoretical, and experimental knowledge from this study is expected to seed many GAN-based farm delineation methods in the future.

Key words: field boundary; contour detection; fully convolutional neural networks; generative adversarial networks