Integrating satellite and street-level images for local climate zone mapping

Cao, Rui; Liao, Cai; Li, Qing; Tu, Wei; Zhu, Rui; Luo, Nancy; Qiu, Guoping; Shi, W.

doi:10.1016/j.jag.2023.103323

Cited by 4 publications

(2 citation statements)

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“…This study differs from many others that have used streetlevel imagery because they first used a pre-trained classifier to segment the images into features. These features are then input to a neural network to learn other specific features of interest, e.g., building types [30] or local climate zones [32]. In contrast, in this study, the images were fed directly into a CNN and classified by crop type in one system.…”

Section: Discussionmentioning

confidence: 99%

“…The use of computer vision and the segmentation of street-level photographs is the subject of an active area of research. For example, Kang et al, (2018) [30] used images from Google Street View to classify building types, Cao et al, (2018) [31] created a land cover map of New York from combining street-level and aerial imagery, while a detailed urban map (of local climate zones) was developed by Cao et al, (2023) [32] using Google Street View. These and other similar studies are largely focused on the mapping of urban areas or features and have used pre-trained DL networks such as Places-CNN to first classify the images.…”

Section: Introductionmentioning

confidence: 99%

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

Orduna-Cabrera,

Sandoval-Gastelum,

McCallum

et al. 2023

Geographies

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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.

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Section: Discussionmentioning

confidence: 99%