#Japan #Tokyo #Qlean Dataset #Visual Bank #Urban Architecture

Visual Bank Unveils Qlean Dataset

Visual Bank Inc., based in Shibuya, Tokyo, is spearheading the introduction of the Qlean Dataset through its subsidiary, Amana Images. This innovative AI training data solution caters to various research and commercial AI development needs. Recently, the company expanded its offerings by adding two new image datasets: one focusing on commercial facilities and streetscapes, and another featuring high-rise and mid-rise buildings. This development enhances their comprehensive lineup of AI training data known as 'Data Recipe.'

What is the Qlean Dataset?

Qlean Dataset stands out due to its selection of original, commercially usable data. Its 'Data Recipe' feature allows users to seamlessly combine data materials based on their specific requirements, such as accuracy and delivery time. The dataset includes both annotated and unannotated data, with customization and expansion options available to meet individual necessities. Partnerships with Chiba Lotte Marines and Toyo Keizai Shinpo, along with a robust national and international network, have facilitated the ongoing enhancement of this dataset.

This initiative significantly reduces the burdens associated with data collection and organization in AI development, thus accelerating project timelines.

Overview of the Commercial Facilities & Streetscape Image Dataset

• - Data Content: Images capturing commercial facilities and surrounding streetscapes throughout Japan.
• - Pixel Dimensions: More than 4,000 pixels in length.
• - File Format: JPEG.
• - Number of Files: 300.

For sample images and more details, visit: Commercial Facilities Dataset.

Overview of the High-Rise & Mid-Rise Building Image Dataset

• - Data Content: Exterior images of various buildings, including high-rise and mid-rise residential complexes and office buildings.
• - Pixel Dimensions: More than 4,000 pixels in length.
• - File Format: JPEG.
• - Number of Files: 7509.

For sample images and more details, visit: High-Rise Buildings Dataset.

Use Cases for Commercial Facilities & Streetscape Dataset

1. Segmentation Model Training: Utilize the dataset for training segmentation models that can automatically distinguish commercial facilities, sidewalks, street trees, vehicles, and people. This aids in the development of visual analysis for smart city initiatives and urban landscape evaluation models.
2. Landscape Evaluation AI Development: Train AI to quantitatively assess the beauty and attractiveness of urban commercial areas based on the images provided, facilitating real estate value predictions and urban development projects' landscape design.
3. Support for Digital Twin Cities: Use the dataset as training data for 3D reconstruction AI and environmental simulators aimed at digitally recreating urban exteriors, particularly effective for modeling real urban commercial areas.
4. Cityscape Generation AI Training: With high-density coverage of streetscape elements, the dataset contributes to image generation and AR/VR model structure learning aimed at reproducing urban spaces.

Use Cases for High-Rise & Mid-Rise Buildings Dataset

1. Building Type Classification Model: Ideal for training AI to classify building types like apartments, offices, and public facilities based on exterior characteristics, beneficial for real estate and urban planning applications.
2. Automated Building Height and Floor Count Estimation: The dataset can serve as training data for models estimating building heights and layers, useful for simulating urban landscape regulations and disaster damage assessments.
3. Building Deterioration Diagnosis AI Training: Leverage comparative images of aging and new structures to train AI for automated diagnostics regarding areas needing maintenance, with potential applications in insurance and facility management sectors.
4. Building Facade Simulation AI Validation Data: The dataset’s myriad perspectives of building shapes, shadows, and angles can assist in validating CG and AR/VR systems in urban redesign or digital twin projects.

Key Features of Qlean Dataset

• - Research and Commercial Use Compliance: Qlean Dataset obtains consent from all subjects for data acquisition and AI development. It adheres to privacy policies of multiple countries, ensuring safe utilization for research and commercial purposes.
• - Speedy and ROI-Maximizing Data Procurement: Utilizing the Data Recipe form, Qlean Dataset allows for cost-efficient data acquisition while maximizing return on investment.
• - Customized Data Set Creation: Datasets beyond the Data Recipe lineup can be individually crafted according to specific requirements, ensuring high uniqueness and tailored solutions.

For inquiries and further study, please visit: Qlean Dataset Contact or explore the Qlean Dataset Service Site.

Upcoming Seminar on AI and Copyright

Visual Bank will host a free online seminar on August 20, 2025, focusing on copyright risks associated with generative AI. Featuring insights from a law firm and the Qlean Dataset head, this event is integral for those interested in the legal responsibilities of AI development.

• - Date and Time: August 20, 2025, from 14:00 to 15:30.
• - Format: Online (free participation).
• - Presenters: Lawyers from ZeLo Law Office and Qlean Dataset officials.
• - Registration link: Seminar Registration.

About Visual Bank Inc.

Visual Bank aims to enhance AI development capabilities by constructing and providing next-generation data infrastructure. With a mission to 'unlock the potential of all data,' Visual Bank offers a variety of services, including the Qlean Dataset by its fully owned subsidiary Amana Images.

• - CEO: Masayuki Nagai
• - Address: 5-3-23 Kojimachi, Chiyoda City, Tokyo, WeWork, Nippon TV Yotsuya Building, 102-0083
• - Official Website: Visual Bank
• - Amana Images Website: Amana Images