Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information

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submitted 102859 1 313978 document 102859 indexcodes.txt text/x-c++ 46f044e94182123adf5005c466705473 MD5 8849 2023-09-07 12:55:58 https://pure.iiasa.ac.at/id/eprint/19041/8/indexcodes.txt 19041 8 8 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/102858 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/102858 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/102858 archive 353 disk0/00/01/90/41 2023-09-07 12:33:59 2025-02-03 12:47:47 2023-09-07 12:33:59 article show Orduña-Cabrera F. 2239 0000-0002-8558-0053 Sandoval-Gastelum M. 9293 McCallum I. 1731 0000-0002-5812-9988 See L. 8571 0000-0002-2665-7065 Fritz S. 8130 0000-0003-0420-8549 Karanam S. 2247 Sturn T. 2051 Javalera Rincón V. 2170 0000-0001-8743-9777 Gonzalez-Navarro F.F. Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information pub ASA EM NODES crop type recognition; deep learning; crowdsourcing; street-level imagery The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators. 2023-08-30 published doi:10.3390/geographies3030029 3130 3532 202 276 98 3224 301 3030 public Geographies 3 3 563-573 TRUE 2673-7086 Open-Earth-Monitor Cyberinfrastructure (OEMC, HE 101059548) yes info:eu-repo/grantAgreement/EC/HORIZON/101059548/EU//OEMC info:eu-repo/semantics/article info:eu-repo/semantics/openAccess