DOI: 10.48436/p2389-6sb06
title: INDIGO Change Detection Reference Dataset

description: The INDIGO Change Detection Reference Dataset
Description
This graffiti-centred change detection dataset was developed in the context of INDIGO, a research project focusing on the documentation, analysis and dissemination of graffiti along Vienna's Donaukanal. The dataset aims to support the development and assessment of change detection algorithms. 
The dataset was collected from a test site approximately 50 meters in length along Vienna's Donaukanal during 11 days between 2022/10/21 and 2022/12/01. Various cameras with different settings were used, resulting in a total of 29 data collection sessions or "epochs" (see "EpochIDs.jpg" for details). Each epoch contains 17 images generated from 29 distinct 3D models with different textures. In total, the dataset comprises 6,902 unique image pairs, along with corresponding reference change maps. Additionally, exclusion masks are provided to ignore parts of the scene that might be irrelevant, such as the background.
To summarise, the dataset, labelled as "Data.zip," includes the following:

Synthetic Images: These are colour images created within Agisoft Metashape Professional 1.8.4, generated by rendering views from 17 artificial cameras observing 29 differently textured versions of the same 3D surface model.
Change Maps: Binary images that were manually and programmatically generated, using a Python script, from two synthetic graffiti images. These maps highlight the areas where changes have occurred.
Exclusion Masks: Binary images are manually created from synthetic graffiti images to identify "no data" areas or irrelevant ground pixels.
Image Acquisition 
Image acquisition involved the use of two different camera setups. The first two datasets (ID 1 and 2; cf. "EpochIDs.jpg") were obtained using a Nikon Z 7II camera with a pixel count of 45.4 MP, paired with a Nikon NIKKOR Z 20 mm lens. For the remaining image datasets (ID 3-29), a triple GoPro setup was employed. This triple setup featured three GoPro cameras, comprising two GoPro HERO 10 cameras and one GoPro HERO 11, all securely mounted within a frame. This triple-camera setup was utilised on nine different days with varying camera settings, resulting in the acquisition of 27 image datasets in total (nine days with three datasets each).
Data Structure
The "Data.zip" file contains two subfolders: 

1_ImagesAndChangeMaps: This folder contains the primary dataset. Each subfolder corresponds to a specific epoch. Within each epoch folder resides a subfolder for every other epoch with which a distinct epoch pair can be created. It is important to note that the pairs "Epoch Y and Epoch Z" are equivalent to "Epoch Z and Epoch Y", so the latter combinations are not included in this dataset. Each sub-subfolder, organised by epoch, contains 17 more subfolders, which hold the image data. These subfolders consist of:

Two synthetic images rendered from the same synthetic camera ("X_Y.jpg"  and "X_Z.jpg")
The corresponding binary reference change map depicting the graffiti-related differences between the two images ("X_YZ.png"). Black areas denote new graffiti (i.e. "change"), and white denotes "no change". "DataStructure.png" provides a visual explanation concerning the creation of the dataset. The filenames follow the following pattern:

X - Is the ID number of the synthetic camera. In total, 17 synthetic cameras were placed along the test site
Y - Corresponds to the reference epoch (i.e. the "older epoch")
Z - Corresponds to the "new epoch" 
2_ExclusionMasks: This folder contains the binary exclusion masks. They were manually created from synthetic graffiti images and identify "no data" areas or areas considered irrelevant, such as "ground pixels". Two exclusion masks were generated for each of the 17 synthetic cameras:

"groundMasks": depict ground pixels which are usually irrelevant for the detection of graffiti
"noDataMasks": depict "background" for which no data is available.  
A detailed dataset description (including detailed explanations of the data creation) is part of a journal paper currently in preparation. The paper will be linked here for further clarification as soon as it is available. 
Licensing
Due to the nature of the three image types, this dataset comes with two licenses:

Synthetic images:

These come with an In Copyright license (for the rights usage terms, see https://rightsstatements.org/page/InC/1.0/?language=en).
The copyright lies with:

the Ludwig Boltzmann Gesellschaft (https://d-nb.info/gnd/1024204324)
the TU Wien (https://d-nb.info/gnd/55426-1)
One or more anonymous graffiti creator(s) upon whose work these images are based.
The first two entities are also the licensor of these images. 
Change maps and masks:

These are openly licensed via CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)
In this case, the copyright lies with:

the Ludwig Boltzmann Gesellschaft (https://d-nb.info/gnd/1024204324)
the TU Wien (https://d-nb.info/gnd/55426-1)
Both institutes are also the licensor of these images.
Every synthetic image, change map and mask has this licensing information embedded as IPTC photo metadata. In addition, the images' IPTC metadata also provide a short image description, the image creator and the creator's identity (in the form of an ORCiD).
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If there are any questions, problems or suggestions for the dataset or the description, please do not hesitate to contact the corresponding author, Benjamin Wild.