{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:49:59Z","timestamp":1775634599501,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,6,4]],"date-time":"2021-06-04T00:00:00Z","timestamp":1622764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Digital agriculture services can greatly assist growers to monitor their fields and optimize their use throughout the growing season. Thus, knowing the exact location of fields and their boundaries is a prerequisite. Unlike property boundaries, which are recorded in local council or title records, field boundaries are not historically recorded. As a result, digital services currently ask their users to manually draw their field, which is time-consuming and creates disincentives. Here, we present a generalized method, hereafter referred to as DECODE (DEtect, COnsolidate, and DElinetate), that automatically extracts accurate field boundary data from satellite imagery using deep learning based on spatial, spectral, and temporal cues. We introduce a new convolutional neural network (FracTAL ResUNet) as well as two uncertainty metrics to characterize the confidence of the field detection and field delineation processes. We finally propose a new methodology to compare and summarize field-based accuracy metrics. To demonstrate the performance and scalability of our method, we extracted fields across the Australian grains zone with a pixel-based accuracy of 0.87 and a field-based accuracy of up to 0.88 depending on the metric. We also trained a model on data from South Africa instead of Australia and found it transferred well to unseen Australian landscapes. We conclude that the accuracy, scalability and transferability of DECODE shows that large-scale field boundary extraction based on deep learning has reached operational maturity. This opens the door to new agricultural services that provide routine, near-real time field-based analytics.<\/jats:p>","DOI":"10.3390\/rs13112197","type":"journal-article","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T01:56:40Z","timestamp":1623031000000},"page":"2197","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Detect, Consolidate, Delineate: Scalable Mapping of Field Boundaries Using Satellite Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5599-7456","authenticated-orcid":false,"given":"Fran\u00e7ois","family":"Waldner","sequence":"first","affiliation":[{"name":"CSIRO Agriculture & Food, St Lucia, QLD 4067, Australia"},{"name":"European Commission Joint Research Centre, 21027 Ispra, VA, Italy"}]},{"given":"Foivos I.","family":"Diakogiannis","sequence":"additional","affiliation":[{"name":"International Centre for Radio Astronomy Research, University of Western Australia, Crawley, WA 6009, Australia"},{"name":"CSIRO Data61, Kensington, WA 6155, Australia"}]},{"given":"Kathryn","family":"Batchelor","sequence":"additional","affiliation":[{"name":"CSIRO Health & Biosecurity, 147 Underwood Avenue, Floreat, WA 6014, Australia"}]},{"given":"Michael","family":"Ciccotosto-Camp","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture & Food, St Lucia, QLD 4067, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4219-9447","authenticated-orcid":false,"given":"Elizabeth","family":"Cooper-Williams","sequence":"additional","affiliation":[{"name":"CSIRO Health & Biosecurity, Australian e-Health Research Centre, Royal Brisbane and Women\u2019s Hospital, Herston, QLD 4029, Australia"}]},{"given":"Chris","family":"Herrmann","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture & Food, 147 Underwood Avenue, Floreat, WA 6014, Australia"}]},{"given":"Gonzalo","family":"Mata","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture & Food, 147 Underwood Avenue, Floreat, WA 6014, Australia"}]},{"given":"Andrew","family":"Toovey","sequence":"additional","affiliation":[{"name":"CSIRO Agriculture & Food, 147 Underwood Avenue, Floreat, WA 6014, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.cosust.2013.06.002","article-title":"Challenges and opportunities in mapping land use intensity globally","volume":"5","author":"Kuemmerle","year":"2013","journal-title":"Curr. 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