Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System

Abstract

Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots. INDEX TERMS Convolutional neural network, deep learning, edge extraction, land parcel identification system, parcels delineation.

U-Net Convolutional Networks for Mining Land Cover Classification Based on High-Resolution UAV Imagery

“…The filter weight and length have to be smaller than those of the input sub-images. [47]. Each input layer is normalized by using the mean ( ) and standard deviation (or variance -) parameter of the values in the current batch based on the following formula: whereas the and are trainable parameters, ̂ can be calculated by using mean ( ) and variance ( 2 ) of mini-batch B = {x1…m} as following formula:…”

A Deep Learning Architecture for Batch-Mode Fully Automated Field Boundary Detection

“…Gracia-Pedrero et al employ a U-Net architecture to segment images into three classes: field, buffered boundary, and background. The boundaries are then computed from the contour of the first class (García-Pedrero et al, 2019). Likewise, in (Waldner, Diakogiannis, 2019), Waldner and Diakogiannis adapt a U-Net model to generate not only segmented images, but also predict distances to the field boundaries.…”

“…Within the LPIS framework, most member states of the European Union makes publicly available datasets of agricultural parcels, which inform on their crop types and geometries. This dataset has been used before in the relevant literature (García-Pedrero et al, 2019). This work also used this source, more specifically, the French and Danish datasets for the year 2018 2 and 2019 3 respectively.…”

Early prediction of coronavirus disease epidemic severity in the contiguous United States based on deep learning

“…Deep learning can reduce the manual manipulation of data as, in this method, the importance of each dataset is automatically managed by neural networks. Several studies have shown that deep learning can solve previously challenging issues in the fields of engineering [17] , manufacturing [18] , [19] , [20] , transportation [21] , [22] , [23] , agriculture [24] , [25] , [26] , and medicine [27] , [28] , [29] , [30] .…”