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License:
CC-BY-NC-SA-4.0
Steward:
CommunityDataset ID:
cmubjs3e500jvnx07n3bpo5i8
Release Date: 9/21/2026
Format: JPEG, WEBM, JSON
Size: 912.96 MB
This dataset contains 477 photos of public spaces, public signs, parking areas, and road situations collected across Blitar, Surabaya, Yogyakarta, Surakarta, and Ngawi, Java Island, Indonesia. The images include hospitals, traffic signs, shopping malls, campuses, restaurants, roads, and motorcycle and car parking areas. The dataset aims to provide visual information about public spaces, public facilities, traffic situations, and urban conditions through varied images collected by the dataset owner or creator.
Licensing
Creative Commons Attribution Non Commercial Share Alike 4.0 International (CC-BY-NC-SA-4.0)
https://spdx.org/licenses/CC-BY-NC-SA-4.0.htmlRestrictions/Special Constraints
Intended for research, educational, and academic purposes. May be used for Computer Vision and AI model development, testing, and evaluation. May be used for research related to image classification and environmental scene recognition. Users are encouraged to provide appropriate attribution to the dataset. Do not re-host or redistribute the dataset as a separate dataset.
Forbidden Usage
The dataset may not be used for unlawful, harmful, or unethical purposes. Users are prohibited from attempting to identify or reveal the identities of people captured in the images. Using the dataset for facial recognition, biometric identification, or unauthorized profiling of individuals is not permitted. Any use of the dataset for unauthorized surveillance or privacy-invading activities is prohibited. The dataset must not be used to generate deceptive, harmful, or misleading content. The dataset may not be used as training data for chatbots or large language models (LLMs). Redistribution or re-hosting of the dataset as an independent dataset is not allowed.
Ethical Review
Images of this dataset are taken and owned by the dataset creator. Images are compiled and annotated using (CaLI) Caption and Label Images from MDC tools: https://mdc-dataset-toolbox-ifuhj.ondigitalocean.app/app/cali
Intended Use
This dataset is designed to support research, education, and AI development in the field of Computer Vision. It is suitable for a variety of visual recognition tasks, including: Image classification and object detection for public places, facilities, roads, parking areas, and recreational environments. Recognition and localization of public and traffic signs. Training, testing, and benchmarking AI and Computer Vision models. Analysis of road scenes, traffic conditions, and public environments for intelligent transportation and smart-city applications. Text recognition and extraction from visible signs and other text-containing objects using OCR techniques. Development of AI-based systems for environmental scene understanding and place recognition.
Images are annotated in Bahasa Indonesia.
477 images
Public open access is permitted with proper attribution and citation of the dataset source.
Lailatul Zunaeva. (2026). Public Space and Sign in Javanese-Speaking Regions [Data set]. Mozilla Data Collective. URL [link dataset].
The data was collected by taking photographs in the field or on the sites and by collecting personal archives. The photographs were taken in several cities in Java Island, Indonesia, including Yogyakarta, Surabaya, Surakarta, Ngawi, Blitar, and Batu. The objects photographed were public spaces and their surroundings, including the facilities, physical situations, activities, and other elements found in the spaces. The photographs used a combination of wide and close-up views. Wide views were used to show the overall condition of the public space, while close-up views were used to capture specific objects and details.
The dataset captures diverse real-world environments in Java Island, Indonesia, including urban public spaces, recreational areas, roads, commercial places, hospitals, and natural surroundings. The images reflect everyday community activities such as transportation, education, shopping, dining, social interaction, and recreation, while also showing regional and cultural diversity across different locations. Public and traffic signs, local architecture, environmental conditions, and Indonesian-language text further contribute to the dataset’s uniqueness and make it representative of everyday visual scenes in Java Island, Indonesia.
The dataset has several limitations that should be considered when using the images for research or AI development. First, image quality and camera conditions may vary, and some objects are not captured clearly or from sufficient angles, resulting in limited multi-perspective coverage. Second, the dataset does not represent all public areas within the selected regions, as image collection was focused on specific locations and visually distinctive areas rather than providing exhaustive geographic coverage. Third, the representation of social and environmental conditions is limited, as the images may not capture the full diversity of activities, situations, weather, lighting, or traffic conditions that occur in public spaces. In addition, the dataset contains a limited number of images for certain types of locations and objects, which may result in an uneven distribution between categories. These limitations should be taken into account when interpreting results or using the dataset to train and evaluate Computer Vision models.
Latin alphabet (A–Z), Arabic numerals (0–9).