Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information DOI Creative Commons
Fernando Orduña-Cabrera,

Marcial Sandoval-Gastelum,

Ian McCallum

и другие.

Geographies, Год журнала: 2023, Номер 3(3), С. 563 - 573

Опубликована: Авг. 30, 2023

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack accurate in situ data. Street-level imagery represents new potential source that may aid mapping, but it requires automated algorithms to recognize the features interest. This paper aims demonstrate method for (i.e., maize, wheat others) recognition street-level based on convolutional neural network using bottom-up approach. We trained model with highly dataset crowdsourced labelled Picture Pile application. classification results achieved an AUC 0.87 wheat, 0.85 maize 0.73 others. Given are two most common food crops grown globally, combined ever-increasing amount available imagery, this approach could help address need improved global monitoring. Challenges remain addressing noise aspect buildings, hedgerows, automobiles, etc.) uncertainties due differences time day location. Such also be applied developing other sets e.g., land use mapping or socioeconomic indicators.

Язык: Английский

Investigating the Use of Street-Level Imagery and Deep Learning to Produce In-Situ Crop Type Information DOI Creative Commons
Fernando Orduña-Cabrera,

Marcial Sandoval-Gastelum,

Ian McCallum

и другие.

Geographies, Год журнала: 2023, Номер 3(3), С. 563 - 573

Опубликована: Авг. 30, 2023

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack accurate in situ data. Street-level imagery represents new potential source that may aid mapping, but it requires automated algorithms to recognize the features interest. This paper aims demonstrate method for (i.e., maize, wheat others) recognition street-level based on convolutional neural network using bottom-up approach. We trained model with highly dataset crowdsourced labelled Picture Pile application. classification results achieved an AUC 0.87 wheat, 0.85 maize 0.73 others. Given are two most common food crops grown globally, combined ever-increasing amount available imagery, this approach could help address need improved global monitoring. Challenges remain addressing noise aspect buildings, hedgerows, automobiles, etc.) uncertainties due differences time day location. Such also be applied developing other sets e.g., land use mapping or socioeconomic indicators.

Язык: Английский

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