Abstract
Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The purpose of the present paper is to demonstrate the potential of the Res-UNet approach for a fast, robust, accurate, and automated field boundary detection approach based on Sentinel-2 data
We propose a ResU-Net convolutional neural network to detect field boundaries in Sentinel-2 imagery
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Accurate agricultural mapping is of fundamental relevance for a wide array of applications [1]. Government agencies and administrative institutions like the European. Union rely on information about the distribution, extent, and size of fields as well as acreage of specific crops to determine subsidies or enforce agricultural regulations [1]. Growing concern about sustainable agricultural practices and reasonable crop management requires new rules to be enforced by law and monitored regularly [2]. This raises the need for up-to-date knowledge about the state of croplands on a regional level [3,4]
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