Deep Learning for automatic outlining agricultural parcels: exploiting the Land Parcel Identification System

García Pedrero, Ángel Mario and Lillo Saavedra, Mario Fernando and Rodríguez Esparragón, Dionisio and Gonzalo Martín, Consuelo (2019). Deep Learning for automatic outlining agricultural parcels: exploiting the Land Parcel Identification System. "IEEE Access", v. 7 ; pp. 158223-158236. ISSN 2169-3536. https://doi.org/10.1109/ACCESS.2019.2950371.

Description

Title: Deep Learning for automatic outlining agricultural parcels: exploiting the Land Parcel Identification System
Author/s:
  • García Pedrero, Ángel Mario
  • Lillo Saavedra, Mario Fernando
  • Rodríguez Esparragón, Dionisio
  • Gonzalo Martín, Consuelo
Item Type: Article
Título de Revista/Publicación: IEEE Access
Date: 2019
ISSN: 2169-3536
Volume: 7
Subjects:
Freetext Keywords: Convolutional neural network, Deep learning, Edge extraction, Land parcel identification system, Parcels delineation
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainCTM2016-77733-RARTeMISat-2UnspecifiedAdvanced Processing of Remote Sensing Data for Monitoring and Sustainable Management of Marine and Terrestrial Resources in Vulnerable Ecosystems

More information

Item ID: 68095
DC Identifier: https://oa.upm.es/68095/
OAI Identifier: oai:oa.upm.es:68095
DOI: 10.1109/ACCESS.2019.2950371
Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8886377&tag=1
Deposited by: Memoria Investigacion
Deposited on: 03 Nov 2021 11:21
Last Modified: 03 Nov 2021 11:21
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