Download the 17K-Graffiti dataset and its pre-trained weights for detecting Graffiti. The dataset provides larger graffiti instances containing a variety of graffiti types and annotated boundary boxes.

[NOTE] To access the dataset, which is only available for academic use, please send us your complete name, a brief description of your project, your advisor's name, and an academic email with a link to your university page.

For additional material regarding Code and data processing, please see the following GitHub repository at

https://github.com/visual-ds/17K-Graffiti

Please cite the published paper, if you find this dataset helpful on your research work:

@conference{visapp22,
author={Bahram Lavi and Eric K. Tokuda and Felipe Moreno-Vera and Luis Gustavo Nonato and Claudio T. Silva and Jorge Poco},
title={17K-Graffiti: Spatial and Crime Data Assessments in São Paulo City},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 4: VISAPP},
year={2022},
pages={968-975},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010883300003124},
isbn={978-989-758-555-5},
}

This data set provides information on the location of sites with graffiti as identified by City staff. Data currencyThis data in City systems is updated in the normal course of business, however priorities and resources determine how fast a change in reality is reflected in the database. The extract on this web site is updated weekly. Data accuracyLocations are identified by address and therefore locations are approximate.

This data set contains selected information for Graffiti service requests currently in progress or resolved within the past six years.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

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.

The Department of Streets & Sanitation's (DSS) Graffiti Blasters crews offer a vandalism removal service to private property owners. This metric tracks the average number of days DSS takes to complete graffiti removal requests per week. Median days to complete requests as well as total number of requests fulfilled per week are available by mousing over columns. Currently the performance target for completing a graffiti removal request is 10 days. For more information on graffiti removal requests, see https://data.cityofchicago.org/d/hec5-y4x5

This page provides information for the Graffiti Removal performance measure.

This data feeds the graffiti daily dashboard.

This dataset maps the location of anti-social graffiti around the University of Edinburgh's central campus. The data was collected over a 2 week period between the 19th May and the 2nd June 2014. The data was collected using a smartphone through an app called Fieldtrip GB (http://fieldtripgb.blogs.edina.ac.uk/). Multiple asset collectors were deployed to use a pre-defined data collection form which allowed users to log the following attributes: Date / Name of asset collector / Type of graffiti (image/tag/words/advert/.....) / What the graffiti was on (building/wall/lamppost/....) / What medium was used (paint/paper/chalk/....) / Density of graffiti / Photograph / Location. The data is by no means complete and realistically captured only around 50% of the graffiti in the study area. It is hoped that this dataset will be updated every 3 months to chart the distribution of graffiti over time. data was collected using the app Fieldtrip GB Once collected, data from multiple asset collectors was merged in FtGB's authoring tool and exported as a CSV file. This was then imported into QGIS and saved as a vector dataset in ESRI Shapefile format. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-06-06 and migrated to Edinburgh DataShare on 2017-02-22.

The Engineering and Transportation Department operates a robust graffiti abatement program. Its strategic approach includes proactive abatement through directed patrols across the City.Tempe performs a yearly survey to identify the number of tags within the City of Tempe. The survey is conducted along several roads from city limit to city limit by the City of Tempe Transportation Maintenance. The average number of tags per mile is calculated to assess graffiti abatement impact and efficacy.This page provides data for the Graffiti Removal performance measure. Summary of annual graffiti survey showing overall average graffiti tags per mile of surveyed road segments.The performance measure dashboard is available at 3.22 Graffiti Removal.Additional InformationSource: Contact: Isaac A. ChaviraContact E-Mail: Isaac_Chavira@tempe.govData Source Type: ExcelPreparation Method: Publish Frequency: AnnualPublish Method: ManualData Dictionary

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Graffiti is an urban phenomenon that is increasingly attracting the interest of the sciences. To the best of our knowledge, no suitable data corpora are available for systematic research until now. The Information System Graffiti in Germany project (Ingrid) closes this gap by dealing with graffiti image collections that have been made available to the project for public use. Within Ingrid, the graffiti images are collected, digitized and annotated. With this work, we aim to support the rapid access to a comprehensive data source on Ingrid targeted especially by researchers. In particular, we present IngridKG, an RDF knowledge graph of annotated graffiti, abides by the Linked Data and FAIR principles. We weekly update IngridKG by augmenting the new annotated graffiti to our knowledge graph. Our generation pipeline applies RDF data conversion, link discovery and data fusion approaches to the original data. The current version of IngridKG contains 460,640,154 triples and is linked to 3 other knowledge graphs by over 200,000 links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications.

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Analysis of ‘DSNY Graffiti Tracking’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/12c4ba52-31f6-41de-840a-a41b70cac624 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Location and resolution of reported incidents of graffiti within NYC. The Graffiti-Free NYC Program removes graffiti and other blight across the five boroughs. Graffiti-Free NYC is a cooperative effort among the NYC Economic Development Corporation, the NYC Department of Sanitation, and the Office of the Mayor. For more info, see: https://www.nycedc.com/program/graffiti-free-nyc. The COVID-19 is having a significant impact on the City’s economy and finances. As of April 21, 2020, to ensure the City can continue to devote resources to essential safety, health, shelter, and food security needs, the City suspended the Graffiti Free NYC program indefinitely. As a result, 311 has suspended processing of graffiti removal service requests.

--- Original source retains full ownership of the source dataset ---

This dataset will be reset and modified on 04/19/2017. Read the full notice of changes in the 'About' section of this dataset. During most of that day, the dataset will be unavailable.

SF311 cases created since 7/1/2008 with location information

Graffiti presents serious urban concerns, often signaling urban decay. This study uses open spatial data to analyze and model graffiti occurrences in terms of street network centrality measures. In particular, betweenness centrality, closeness centrality, and degree centrality are evaluated using San Francisco, California, as the case study area, with data from OpenStreetMap and reported graffiti from 2008 to 2023 from the San Francisco nonemergency municipal service (denoted as 311) as the data sets. The spatial error model was found to outperform both ordinary least squares tests and the spatial lag model. The model could further explain graffiti spatiality. Graffiti writers were observed to favor street segments that are close to the downtown and well-connected to other streets, often having high accessibility, visibility, and accommodating street furniture. The results indicate that bridges and highway segments that are difficult to stop and tag were typically avoided. In addition, for a given street, the model error in adjacent streets significantly (p 

Abstract:Tabular aggregation of the amount of graffiti removed each month by the City of Victorville Public Works Department.Purpose:This data is an aggregation of graffiti removal and is not meant to display location based data for liability reasons.Supplemental Information:This data is provided by the City of Victorville Public Works Department and is displayed in a table using the City's Geographic Information System.Last Updated:November 7, 2018

This dataset shows the location of Graffiti across the City of Casey that has been removed since 2015.

This dataset contains all graffiti incidents (on all land types) in York recorded in City of York Council’s customer relationship management (CRM) tool from November 2019 onwards. Please note the dataset excludes incidents created in the last 14 days and that incidents with no end date are currently unresolved. For further information about graffiti and reporting graffiti problems please see the City of York Council’s website.

*Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset. The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

This dataset contains the _ most recent_ graffiti incidents (on all land types) in York - covering a 30 days period. The information presented has been recorded in City of York Council’s customer relationship management (CRM) tool from November 2019 onwards. Please note the dataset excludes incidents created in the last 14 days. For all graffiti incidents - unresolved and closed ones, please see the Graffiti - All Incidents dataset. For further information about graffiti and reporting graffiti problems please see the City of York Council’s website.

*Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset. The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.