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Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning

1
Agrosatélite Applied Geotechnology, Florianópolis SC 88050-000, Brazil
2
Imazon—Instituto do Homem e Meio Ambiente da Amazônia, Belém PA 66055-200, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 558; https://doi.org/10.3390/rs12030558 (registering DOI)
Received: 13 December 2019 / Revised: 31 January 2020 / Accepted: 3 February 2020 / Published: 7 February 2020
(This article belongs to the Special Issue Deep Learning and Remote Sensing for Agriculture)
The availability of freshwater is becoming a global concern. Because agricultural consumption has been increasing steadily, the mapping of irrigated areas is key for supporting the monitoring of land use and better management of available water resources. In this paper, we propose a method to automatically detect and map center pivot irrigation systems using U-Net, an image segmentation convolutional neural network architecture, applied to a constellation of PlanetScope images from the Cerrado biome of Brazil. Our objective is to provide a fast and accurate alternative to map center pivot irrigation systems with very high spatial and temporal resolution imagery. We implemented a modified U-Net architecture using the TensorFlow library and trained it on the Google cloud platform with a dataset built from more than 42,000 very high spatial resolution PlanetScope images acquired between August 2017 and November 2018. The U-Net implementation achieved a precision of 99% and a recall of 88% to detect and map center pivot irrigation systems in our study area. This method, proposed to detect and map center pivot irrigation systems, has the potential to be scaled to larger areas and improve the monitoring of freshwater use by agricultural activities. View Full-Text
Keywords: semantic segmentation; neural networks; water management; artificial intelligence semantic segmentation; neural networks; water management; artificial intelligence
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MDPI and ACS Style

Saraiva, M.; Protas, É.; Salgado, M.; Souza, C. Automatic Mapping of Center Pivot Irrigation Systems from Satellite Images Using Deep Learning. Remote Sens. 2020, 12, 558.

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