Urban-ImageNet

Urban-ImageNet is a large-scale multimodal dataset and benchmark for urban commercial space perception. It contains more than 2 million public Weibo image-text pairs collected from 61 commercial sites in 24 Chinese cities across 2019-2025. The dataset is organized by the HUSIC taxonomy, a 10-class framework for urban commercial imagery, and supports three benchmark tasks:

• T1 Urban scene semantic classification
• T2 Cross-modal image-text retrieval
• T3 Instance segmentation

The release provides balanced 1K, 10K, and 100K subsets for reproducible benchmarking, plus a full unbalanced 2M corpus for large-scale training and scaling behavior studies.

Dataset Variants

Variant	Images	Class Balance	Predefined Split	Intended Use
1K Dataset	1,000	100 images per class	train/val/test	Quick tests, demos, debugging
10K Dataset	10,000	1,000 images per class	train/val/test	Medium-scale experiments
100K Dataset	100,000	10,000 images per class	train/val/test	Main benchmark split
Full Dataset-2M	2M+	Natural unbalanced distribution	No predefined split	Large-scale training and custom splitting

The 1K, 10K, and 100K variants share the same three-task structure:

1K Dataset/
  01 Images with labels/
    train/{HUSIC class name}/*.jpg
    val/{HUSIC class name}/*.jpg
    test/{HUSIC class name}/*.jpg
  02 Text-Image Pairs/
    train.xlsx
    val.xlsx
    test.xlsx
  03 Instance Segmentation/
    train.json
    val.json
    test.json
    Visualization of annotation samples/  # optional qualitative examples

The full corpus uses a flatter structure:

Full Dataset-2M/
  Images/
    *.jpg
  Labels/
    01 Semantic classification labels.CSV
    02 Text-Image Pairs.CSV
    03 Instance Segmentation labels.CSV

All released images are privacy-protected and resized to a maximum long edge of 512 px.

HUSIC Classes

ID	Class Label	Group	Meaning
0	Exterior urban spaces with people	Exterior	Outdoor commercial spaces with visible human presence
1	Exterior urban spaces without people	Exterior	Outdoor architecture or public-realm views without people
2	Interior urban spaces with people	Interior	Commercial interiors with shoppers, workers, or occupants
3	Interior urban spaces without people	Interior	Interior commercial spaces focused on design or circulation
4	Hotel or commercial lodging spaces	Accommodation	Hotel rooms and commercial lodging environments
5	Private home interiors	Accommodation	Private residential interiors in the broader urban corpus
6	Food or drink items	Consumption	Food, beverages, dining-table scenes, and restaurant content
7	Retail products and merchandise	Consumption	Products, merchandise, retail shelves, and display windows
8	Human-centered portrait	Portrait	Selfies, group photos, and portrait-dominant images
9	Other non-spatial content	Miscellaneous	Ads, screenshots, memes, maps, animals, and other non-spatial content

Task 1: Urban Scene Semantic Classification

Task 1 uses the 01 Images with labels folder. Images are arranged in an ImageFolder-style hierarchy:

train/{class_name}/{image_filename}.jpg
val/{class_name}/{image_filename}.jpg
test/{class_name}/{image_filename}.jpg

The folder name is the ground-truth HUSIC label. The same label is also available in the Image Label column of the text-image pair files.

Task 2: Cross-Modal Image-Text Retrieval

Task 2 uses the 02 Text-Image Pairs files. Each Excel file contains image-level rows that can be joined to image files by the Image Filename column. The file stem matches the image filename in 01 Images with labels.

For example:

Image Filename = 2668383_2020-01-21_0
Image file     = 2668383_2020-01-21_0.jpg

The released dataset preserves the original Chinese Weibo text to avoid translation distortion. English text shown in papers, examples, or documentation is illustrative and should not be treated as released ground truth.

Text-Image Pair Columns

Column	Description	Task Role
Image Label	HUSIC class label for the image	T1 label and T2 category-level text
Image Filename	Join key linking spreadsheet rows to image files	Join key
Post ID	Anonymized numerical post identifier	Metadata
User ID	Anonymized numerical user identifier; original usernames are not released	Metadata
Post Time	Original post timestamp	Metadata
Post Text	Original Chinese Weibo post text	T2 post-level text
City	City associated with the location tag	Metadata
Place Tag	Location hashtag or commercial-site place tag	Metadata
Posting Tool	Client or posting-source string after metadata minimization	Metadata
Mentioned Users	Anonymized, minimized, or empty mentioned-user field	Metadata
Extracted Topics	Topic or hashtag terms extracted from the post text	Metadata
Extracted Locations	Location mentions extracted from the post text	Metadata
Like Count	Public engagement count at collection time	Metadata
Repost Count	Public repost count at collection time	Metadata
Comment Count	Public comment count at collection time	Metadata

T2 Evaluation Settings

Urban-ImageNet supports two image-text matching settings:

Setting	Text Query	Ground Truth	Notes
T2-A Category-level retrieval	HUSIC label text, such as Exterior urban spaces with people	Images with the same Image Label	Easier structured semantic alignment
T2-B Post-level retrieval	Original Chinese Post Text	Images attached to the same post	Harder, because one post can contain up to 9 images and the text is not always a literal caption

Task 2 can be used in either direction:

• Image-to-text: input an image, retrieve the matching HUSIC label or post text.
• Text-to-image: input a HUSIC label or post text, retrieve one or more matching images.

For post-level retrieval, one post may map to multiple images. Evaluation should use multi-positive ground truth rather than assuming a one-to-one caption-image relationship.

Task 3: Instance Segmentation

Task 3 uses the 03 Instance Segmentation JSON files. The format is COCO-style and includes:

• info: split and annotation metadata
• categories: the 10 HUSIC classes
• images: image ID, file name, width, height, and classification_label
• annotations: category_id, detected_label, bbox, area, COCO RLE segmentation, iscrowd, and detection_score

Instance pseudo-labels were generated with Grounding DINO and SAM2 using class-specific prompt vocabularies. They are model-generated annotations, not exhaustive human pixel-level labels. Users should account for this distinction when training or evaluating segmentation models.

Privacy and Responsible Use

Urban-ImageNet is derived from public Weibo posts. Although the source posts were public, the release uses privacy-protected derivatives:

• Original usernames and account names are removed.
• Post ID and User ID are opaque numerical identifiers after anonymization/pseudonymization.
• Faces, license plates, QR-code-like regions, and other sensitive visual regions are blurred.
• Released images are resized to a maximum long edge of 512 px.
• The raw high-resolution corpus, larger than 4 TB, is not publicly released.
• Metadata is minimized to support research while reducing re-identification risk.

The dataset is intended for non-commercial academic research in urban perception, computational urban studies, multimodal learning, image classification, image-text retrieval, and segmentation.

Prohibited uses include re-identification, account reconstruction, face recognition, surveillance, social scoring, law-enforcement targeting, commercial profiling, and demographic inference about specific individuals.

Limitations and Biases

• The corpus is China-centered and should not be treated as globally representative.
• Weibo users are not representative of all city users.
• Social-media images overrepresent photogenic, popular, and personally meaningful scenes.
• Post text is original Chinese social-media language and contains slang, hashtags, and loose image-text coupling.
• The full 2M corpus is naturally class-imbalanced.
• The 1K, 10K, and 100K subsets are balanced for benchmarking and therefore do not reflect natural class frequencies.
• Task 3 masks are pseudo-labels generated by Grounding DINO and SAM2.

Citation

@misc{urbanimagenet2026,
  title  = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset for Urban Space Perception Benchmarking},
  author = {Urban-ImageNet Research Team},
  year   = {2026},
  note   = {Dataset and benchmark for NeurIPS 2026 Evaluations and Datasets Track}
}