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

Fernando Orduna-Cabrera (), Marcial Sandoval-Gastelum, Ian McCallum, Linda See, Steffen Fritz, Santosh Karanam, Tobias Sturn, Valeria Javalera-Rincon and Felix F. Gonzalez-Navarro
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Fernando Orduna-Cabrera: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Marcial Sandoval-Gastelum: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Ian McCallum: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Linda See: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Steffen Fritz: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Santosh Karanam: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Tobias Sturn: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Valeria Javalera-Rincon: International Institute for Applied Systems Analysis, A-2361 Laxenburg, Austria
Felix F. Gonzalez-Navarro: Instituto de Ingeniería, Universidad Autónoma de Baja California, Mexicali 21000, Mexico

Geographies, 2023, vol. 3, issue 3, 1-11

Abstract: 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.

Keywords: crop type recognition; deep learning; crowdsourcing; street-level imagery (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2023
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