RIT-18

Download RIT-18 Data Set

Description

As presented by R. Kemker, C. Salvaggio, and C. Kanan in "Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning", ISPRS Journal of Photogrammetry and Remote Sensing, 145, pp. 60-77, the RIT-18 data set is a high-resolution (4.7 cm) benchmark, designed to evaluate the semantic segmentation of multispectral imagery (MSI), collected by a small unmanned aircraft systems sUAS. sUAS collection of non-RGB imagery has grown in popularity, especially in precision agriculture, because it is more cost effective than manned flights and provides better spatial resolution than satellite imagery. This cost savings allows the user to collect data more frequently, which increases the temporal resolution of the data as well. The applications for sUAS with MSI payloads include crop health sensing, variable-rate nutrient application prescription, irrigation engineering, and crop-field variability(1).

1) Y. Huang, S.J. Thomson, Y. Lan, and S.J. Maas, "Multispectral imaging systems for airborne remote sensing to support agricultural production management", Int. J. Agric. Biol. Eng. 3 (1), pp. 50–62.

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