Abstract
Abstract. The accurate split of large areas of land into discrete fields is a crucial step for several agriculture-related remote sensing pipelines. This work aims to fully automate this tedious and resource-demanding process using a state-of-the-art deep learning algorithm with only a single Sentinel-2 image as input. The Mask R-CNN, which has forged its success upon instance segmentation for objects from everyday life, is adapted for the field boundary detection problem. Such model automatically generates closed geometries without any heavy post-processing. When tested with satellite imagery from Denmark, this tailored model correctly predicts field boundaries with an overall accuracy of 0.79. Besides, it demonstrates a robust knowledge generalisation with positive results over different geographies, as it gets an overall accuracy of 0.71 when used over areas in France.
Highlights
An accurate knowledge of field boundaries is a requirement for many actors in agriculture
In this work we present a technique which is envisaged as the first step towards a systematic field boundary detection pipeline
In the rest of the section, we describe the model architecture that needs to be implemented with bespoke adjustments for the field boundary detection problem
Summary
An accurate knowledge of field boundaries is a requirement for many actors in agriculture. Amongst many applications, it is a prerequisite input for farmers to on-board fields on farm management software services, it improves the accuracy of crop type classification (Peña-Barragán et al, 2011, De Wit, Clevers, 2004), and it is used from government agencies to monitor subsidies and farming practices. The collection of these geographical data is obtained by manual labelling of aerial or satellite imagery. This slow, repetitive, and error-prone acquisition hinders scalability. It prevents the batch-mode boundary delineation in large areas. The scientific community has been exploring solutions to accurately and reliably generate field boundaries in a large-scale manner, without intensive user involvement
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