ARCA (Automatic Recognition of Color for Archaeology):
a Desktop Application for Munsell Estimation

19th International Conference on Image Analysis and Processing (ICIAP), 2017

Filippo LM Milotta1,2, Filippo Stanco2, Davide Tanasi1

1University of Catania, Department of Mathematics and Computer Science - Italy, {milotta, fstanco}
2University of South Florida, Center for Virtualization and Applied Spatial Technologies (CVAST) & Department of History - Florida, USA,,

ARCA108 Image Dataset

NOTE: A new extended dataset named ARCA1488 is available at

==> Download from here the ARCA108 Image Dataset. (Last Updated 06/14/17)


Archaeologists are used to employing the Munsell Soil Charts on cultural heritage sites to identify colors of soils and retrieved artifacts. The standard practice of Munsell estimation exploits the Soil Charts by visual means. This procedure is error prone, time consuming and very subjective. To obtain an accurate estimation the process should be repeated multiple times and possibly by other users, since colors might not be perceived uniformly by different people. Hence, a method for objective and automatic Munsell estimation would be a valuable asset to the field of archaeology.
In this work we present ARCA: Automatic Recognition of Color for Archaeology, a desktop application for Munsell estimation. The following pipeline for Munsell estimation aimed towards archaeologists has been proposed: image acquisition of specimens, manual sampling of the image in the ARCA desktop application, automatic Munsell estimation of the sampled points and creation of a sampling report.
A dataset, called ARCA108, consisting of 108 images (and 22,848 samples) has been gathered, in an unconstrained environment, and evaluated with respect to the Munsell Soil Charts.

Dataset Acquisition

No strict constraints have been added in the acquisition phase, in order to allow an easy replicability of the process shown in this work. Two kinds of devices have been employed in our experiments: a professional reflex and a common smartphone.
The reflex model was a Canon EOS 1200D (mounting an EFS 18-55mm zoom lens model) with a resolution of 18 megapixels, while the smartphone model was a Nexus 5X with a main camera resolution of 12.2 megapixels.
The subjects of the taken pictures were the following Munsell Soil Color Charts (Year 2000 Revised Washable Edition): GLEY1, GLEY2, 10R, 2.5YR, 5YR, 7.5YR, 10YR, 2.5Y, 5Y. A Gretag-Macbeth color checker has been also employed, in order to evaluate the gains of have reference colors during photos acquisition.

Our acquisition was set in Tampa, Florida (US), in an almost sunny day, with some cloud cover. It was performed with an unguided approach, so without any fixed positions or angles of view for the camera or subjects.
We acquired the 9 charts of the Munsell Soil Color Charts, with the following possible settings:

- 2 kinds of devices: professional DSLR (Digital Single Reflex Camera) and common smartphone;
- 3 automatic white balancing algorithms (executed by the devices in the image capture phase): automatic, sunny (corresponding to standard illuminant D65: 6,500 K°) and cloudy (corresponding to standard illuminant D75: 7,500 K°);
- 1 fluorescence presetting: direct sunlight;
- 1 ISO setting: 400 ISO;
- 1 focus setting: autofocus;
- 2 kind of subject: the chart itself and the chart with a Gretag-Macbeth color checker nearby.

In this way, we obtained a total of 12 configurations for each Munsell chart, gaining a total of 108 images. The resolution of the images is 5184 X 3456 pixels and 3840 X 2160 pixels for pictures taken by a DSLR camera and smartphone, respectively. All the images were saved in the standard JPG format, with a lossless setting for the quality (the highest possible).