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image-to-text - - text-to-image - - zero-shot-image-classification - - image-segmentation -modalities: - - image - - text language: - - zh - - en +- zh +- en +license: cc-by-nc-4.0 size_categories: - - 1M - arXiv - GitHub - HuggingFace -

- -> Urban-ImageNet fills a critical gap between computer vision and urban studies by treating cities not simply as visual scenes, but as lived, socially produced, and experientially activated spaces. - ---- - +[Paper](https://huggingface.co/papers/2605.09936) | [Code](https://github.com/yiasun/dataset-2) -## Overview +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: -**ImageNet** taught models to recognise objects. **Urban-ImageNet** teaches them to understand how people *experience* cities. +- **T1 Urban scene semantic classification** +- **T2 Cross-modal image-text retrieval** +- **T3 Instance segmentation** -General-purpose benchmarks such as ImageNet and Places365 identify *what is in a scene*, but they were never designed to answer the question that matters in urban studies: *how do people inhabit, narrate, and socially activate urban space?* Urban-ImageNet is a domain-specific complement β€” a 2-million image–text benchmark drawn from real social media, organised by **HUSIC** (Hierarchical Urban Space Image Classification), a 10-class taxonomy grounded in the urban theories of Lefebvre, Gehl, and Newman, to capture the spatial, social, and functional distinctions that matter in urban research. - -The corpus contains over **2 million** public Weibo image–text pairs collected from **61 urban commercial sites** across **24 Chinese cities** spanning **2019–2025**, with controlled benchmark subsets at 1K, 10K, and 100K scale, and a full 2M corpus for large-scale training. The benchmark supports three tasks within one standardised library (**Urban-ImageNet-lib**): - -| # | Task | Input β†’ Output | -| ------ | ----------------------------------- | ------------------------------------- | -| **T1** | Urban scene semantic classification | Image β†’ HUSIC label (0–9) | -| **T2** | Cross-modal image–text retrieval | Image ↔ Text (bidirectional) | -| **T3** | Instance segmentation | Image β†’ Object masks + bounding boxes | - -![Urban-ImageNet Framework Overview](Figures/01-Overall-Framework.jpg) -*Figure 1: The Urban-ImageNet framework β€” addressing current limitations in urban perception evaluation. The dataset bridges general-purpose vision benchmarks and domain-specific urban research needs through the HUSIC taxonomy and three unified benchmark tasks.* - ---- +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 -Four tiers are released to support model development and scaling-behaviour studies: - -| Variant | Total Images | Class Balance | Images per Class | Predefined Split | Storage (512 px) | Primary Use | -| ------------------- | -----------: | ------------------- | ---------------: | ------------------- | ---------------- | ------------------------------------- | -| **Sample Dataset** | 100 | βœ… Balanced | 10 | train only | ~21 MB | Data quality preview, quick inspection | -| **1K Dataset** | 1,000 | βœ… Balanced | 100 | train / val / test | ~87 MB | Quick tests, demos, debugging | -| **10K Dataset** | 10,000 | βœ… Balanced | 1,000 | train / val / test | ~700 MB | Medium-scale experiments | -| **100K Dataset** | 100,000 | βœ… Balanced | 10,000 | train / val / test | ~6 GB | **Main benchmark** | -| **Full Dataset-2M** | 2,000,000+ | ❌ Natural imbalance | Varies | None β€” custom split | ~140+ GB | Large-scale training, scaling studies | - -For all balanced tiers the train/val/test split ratio is **80:10:10**. All three tasks share **identical image files** across tiers; only labels and metadata files differ. The 2M corpus provides per-class image counts to support informed use under realistic class imbalance. - ---- - -## Sample Dataset - -A **100-image representative sample** of Urban-ImageNet is available as a separate HuggingFace repository for those who wish to quickly inspect data quality, explore file formats, and understand annotation conventions before working with the full corpus. - -The sample was constructed by **stratified random sampling** from the `train` split of the 100K Dataset: **10 images were drawn uniformly at random from each of the 10 HUSIC classes**, yielding a perfectly balanced 100-image subset. All three task annotations are fully included for every sampled image, and instance segmentation mask visualisations are provided for all 100 images. - -| Property | Sample Dataset | -|----------|:---:| -| Images | 100 (10 per class) | -| Source split | 100K Dataset β€” train | -| T1 scene labels | βœ… subfolder structure | -| T2 text–image pairs | βœ… single Excel file | -| T3 instance segmentation | βœ… COCO JSON + mask visualisations | - -All 100 images underwent manual human review on top of the automated privacy-protection pipeline. See the [Sample Dataset README](https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet/blob/main/Sample%20Dataset/Sample%20Dataset-README.md) for full details. - ---- - -## File Structure - -### Balanced Tiers (1K / 10K / 100K) - +| 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: + +```text +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 ``` -{Tier} Dataset/ -β”œβ”€β”€ 01 Images with labels/ ← Task 1: Scene Classification -β”‚ β”œβ”€β”€ train/ -β”‚ β”‚ β”œβ”€β”€ Exterior urban spaces with people/ -β”‚ β”‚ β”‚ └── *.jpg -β”‚ β”‚ β”œβ”€β”€ Exterior urban spaces without people/ -β”‚ β”‚ β”‚ └── *.jpg -β”‚ β”‚ β”œβ”€β”€ Food or drink items/ -β”‚ β”‚ β”œβ”€β”€ Hotel or commercial lodging spaces/ -β”‚ β”‚ β”œβ”€β”€ Human-centered portrait/ -β”‚ β”‚ β”œβ”€β”€ Interior urban spaces with people/ -β”‚ β”‚ β”œβ”€β”€ Interior urban spaces without people/ -β”‚ β”‚ β”œβ”€β”€ Other non-spatial content/ -β”‚ β”‚ β”œβ”€β”€ Private home interiors/ -β”‚ β”‚ └── Retail products and merchandise/ -β”‚ β”œβ”€β”€ val/ (same structure) -β”‚ └── test/ (same structure) -β”‚ -β”œβ”€β”€ 02 Text-Image Pairs/ ← Task 2: Cross-Modal Retrieval -β”‚ β”œβ”€β”€ train.xlsx -β”‚ β”œβ”€β”€ val.xlsx -β”‚ └── test.xlsx -β”‚ -└── 03 Instance Segmentation/ ← Task 3: Instance Segmentation - β”œβ”€β”€ train.json - β”œβ”€β”€ val.json - β”œβ”€β”€ test.json -``` - -### Full Corpus (2M) +The full corpus uses a flatter structure: -``` +```text Full Dataset-2M/ -β”œβ”€β”€ Images/ ← All 2M+ images (flat, no subfolders) -β”‚ └── *.jpg -└── Labels/ - β”œβ”€β”€ 01 Semantic classification labels.CSV - β”œβ”€β”€ 02 Text-Image Pairs.CSV - └── 03 Instance Segmentation labels.json + Images/ + *.jpg + Labels/ + 01 Semantic classification labels.CSV + 02 Text-Image Pairs.CSV + 03 Instance Segmentation labels.CSV ``` -> **Image format:** All released images are JPEG, privacy-protected, and resized to a maximum long edge of **512 px** (short edge scaled proportionally). Original usernames, faces, licence plates, and QR codes have been removed or blurred. - ---- - -## The HUSIC Framework - - -![HUSIC 10-Class Taxonomy](Figures/02-HUSIC-Framework.png) -*Figure 2: The HUSIC 10-class hierarchical taxonomy. Classes are organised into two primary groups (Spatially Relevant / Non-Spatially Relevant) and five secondary groups. Manual annotation by three trained researchers achieved Cohen's ΞΊ = 0.87.* - -Raw location-tagged social media content is inherently heterogeneous. A user posting under a single hashtag such as `#Beijing Sanlitun` produces content spanning architectural photography, dining imagery, merchandise displays, selfies, hotel promotion, and noise. Without a principled framework, downstream spatial analyses are confounded by this heterogeneity. **HUSIC** resolves this by providing a theoretically grounded taxonomy that simultaneously serves as a UGC filtering pipeline and a 10-way classification benchmark. - -### Theoretical Grounding +All released images are privacy-protected and resized to a maximum long edge of 512 px. -HUSIC class boundaries are defined by domain-expert concepts rather than data-driven frequency, drawing on three complementary bodies of urban theory: +## HUSIC Classes -- **Lefebvre's Production of Space** β€” The distinction between *conceived space* (design intent) and *lived space* (social appropriation through use) motivates the *with/without people* axis within each spatial group, a distinction absent from all existing vision benchmarks. -- **Gehl's Public Life Studies** β€” Gehl's finding that social activity is both an indicator and a self-reinforcing generator of successful public space justifies treating activated and non-activated spaces as analytically distinct categories. -- **Newman's Spatial Hierarchy** β€” Newman's defensible-space framework, which conceptualises urban environments along a public-to-private gradient, provides the basis for HUSIC's three-tier spatial hierarchy: publicly accessible spaces, transitional semi-public spaces, and privately controlled spaces. - -### HUSIC Class Definitions - -| ID | Class Label | Primary Category | Secondary Group | Description | -| ---: | ---------------------------------------- | ---------------------- | --------------------- | ------------------------------------------------------------ | -| 0 | **Exterior urban spaces with people** | Spatially Relevant | Urban Exterior | Populated plazas, active streetscapes, occupied public spaces with visible human presence | -| 1 | **Exterior urban spaces without people** | Spatially Relevant | Urban Exterior | Empty building facades, vacant streets, unpopulated plazas focusing on architectural features | -| 2 | **Interior urban spaces with people** | Spatially Relevant | Urban Public Interior | Active shopping areas, occupied commercial interiors, indoor events, occupied restaurants | -| 3 | **Interior urban spaces without people** | Spatially Relevant | Urban Public Interior | Empty retail spaces, vacant corridors, interior design and spatial composition views | -| 4 | **Hotel or commercial lodging spaces** | Spatially Relevant | Accommodation | Hotel rooms, serviced apartments, Airbnb-style lodging interiors | -| 5 | **Private home interiors** | Spatially Relevant | Accommodation | Private residential interiors posted in association with nearby urban commercial sites | -| 6 | **Food or drink items** | Non-Spatially Relevant | Consumption | Plated dishes, beverages, dining-table scenes, food presentations | -| 7 | **Retail products and merchandise** | Non-Spatially Relevant | Consumption | Fashion items, electronics, cosmetics, product displays, store-window arrangements | -| 8 | **Human-centered portrait** | Non-Spatially Relevant | Social Portrait | Selfies, group photos, portrait-dominant images with urban backgrounds | -| 9 | **Other non-spatial content** | Non-Spatially Relevant | Miscellaneous | Advertisements, screenshots, memes, maps, infographics, animal photos | - -### Design Philosophy: A Foundation Framework, Not a Closed Taxonomy - -HUSIC intentionally operates at a **10-class foundation level** rather than providing exhaustive fine-grained subcategories. This is a deliberate design framework for various downstream urban studies. - -The primary purpose of these 10 classes is to serve as a **universal filtering and routing layer** for social media imagery: they cleanly partition the semantic space of urban UGC so that researchers can isolate the specific subset relevant to their question, discard noise, and then apply domain-specific analysis on a purified corpus. The downstream subcategorisation β€” which varies enormously across research questions β€” is intentionally left open for the research community to define. - -Filtering to a single HUSIC class typically eliminates **up to 90% of irrelevant content** before any domain-specific analysis begins, dramatically improving the signal-to-noise ratio regardless of the downstream task. To further support deeper analysis within each class, HUSIC is complemented by **Task 3 instance segmentation**, which provides 12–20 class-specific object labels per HUSIC category. This gives researchers both a clean high-level routing layer and a set of object-level semantic anchors without requiring exhaustive fine-grained annotation at the dataset level. - -Some examples of how individual HUSIC classes can seed specialised downstream research: - -| HUSIC Class | Research Direction | Possible Next Step | -|-------------|-------------------|--------------------| -| ID 0 β€” Exterior *with people* | Human behaviour in public space | Pose estimation, action recognition, pedestrian counting; age/gender distribution; temporal activity mapping | -| ID 1 β€” Exterior *without people* | Architectural perception and design quality | Further classify by style (classical / modernist / parametric), space type (plaza / park / streetscape), or faΓ§ade material | -| ID 2 β€” Interior *with people* | Commercial interior vitality | Crowd density, dwell behaviour, wayfinding; how spatial layout influences visitor flow | -| ID 3 β€” Interior *without people* | Retail design and aesthetic perception | Classify by interior style, lighting, or spatial configuration | -| ID 4 β€” Hotel / lodging | Urban tourism and short-term rental market | How commercial district proximity influences lodging aesthetics and pricing signals | -| ID 5 β€” Private home interiors | Urban housing and long-term rental market | How proximity to commercial hubs shapes residential presentation and rental marketing | -| ID 6 β€” Food or drink | F&B consumption trends | Classify by cuisine, price tier, format; UGC posting frequency as a revealed-preference signal of popularity | -| ID 7 β€” Retail products | Consumer behaviour and market analysis | Category trends (fashion / electronics / cosmetics); temporal trend tracking | -| ID 8 β€” Human-centered portrait | Social behaviour and place attachment | Which spatial settings motivate photographic self-documentation; social gathering pattern analysis | -| ID 9 β€” Other non-spatial | Noise filtering | Exclude from virtually all downstream spatial or commercial analyses | - -### Rationale: Spatially Relevant Classes (IDs 0–5) - -The spatially relevant classes cover the full spectrum of urban environments from public exterior, through public interior, to semi-public and private spaces. The **with/without people** split within exterior and interior classes is analytically essential: - -- *With people* classes (IDs 0, 2) enable research on pedestrian behaviour, social activity patterns, human action recognition, and spatial vitality measurement. With pose estimation and action recognition techniques, these images can reveal how different user groups occupy public space and what kinds of activities different spatial configurations support. -- *Without people* classes (IDs 1, 3) isolate architectural and design elements independent of human activity β€” supporting aesthetic perception studies, visual quality assessment, and design feature analysis without the confound of human occlusion. - -The accommodation classes (IDs 4–5) capture a phenomenon consistently observed in the corpus: users post hotel and private-home interiors alongside commercial-district imagery, reflecting how urban commercial centres influence both short-term (hotel / Airbnb) and long-term (residential) rental markets in their surroundings β€” a research direction not addressed by any existing benchmark. - -### Rationale: Non-Spatially Relevant Classes (IDs 6–9) - -Non-spatial classes capture the consumption and social dimensions of urban life equally present in social media posts about commercial districts, even when images contain no spatial content. - -- **ID 6 β€” Food or drink items:** UGC food images carry implicit preference signals β€” frequency of posting reflects popularity and memorability without requiring explicit ratings. Useful for F&B market research and restaurant analytics. -- **ID 7 β€” Retail products and merchandise:** Product images reveal consumer preferences and brand visibility. Because posts originate from people who visited specific commercial sites, the corpus carries an implicit spatial anchor useful for retail market analysis. -- **ID 8 β€” Human-centered portrait:** Self-portraits and group photos document the social occasions that spaces enable β€” revealing which settings activate gathering, self-expression, and place attachment. Analysing when and where people choose to photograph themselves is itself evidence of spatial vitality. -- **ID 9 β€” Other non-spatial content:** A residual noise class β€” advertisements, screenshots, memes, infographics. Minimal relevance for urban or commercial research; its isolation as a discrete class makes downstream filtering reliable and auditable. - ---- +| 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 -**Goal:** Given an input image, predict its HUSIC label (class ID 0–9). - -**File format:** ImageFolder-style hierarchy under `01 Images with labels/`. The subdirectory name is the ground-truth label. Integer labels 0–9 follow lexicographic sort, directly compatible with PyTorch `torchvision.datasets.ImageFolder`. - -```python -from torchvision.datasets import ImageFolder -from torchvision import transforms +Task 1 uses the `01 Images with labels` folder. Images are arranged in an ImageFolder-style hierarchy: -dataset = ImageFolder( - root="100K Dataset/01 Images with labels/train", - transform=transforms.Compose([ - transforms.Resize(224), - transforms.CenterCrop(224), - transforms.ToTensor(), - ]) -) -# dataset.classes β†’ ['Exterior urban spaces with people', 'Exterior urban spaces without people', ...] -# dataset.class_to_idx β†’ {'Exterior urban spaces with people': 0, ...} +```text +train/{class_name}/{image_filename}.jpg +val/{class_name}/{image_filename}.jpg +test/{class_name}/{image_filename}.jpg ``` -### T1 Baseline Results (100K benchmark, 80K/10K/10K split) - -| Model | Top-1 Acc. (%) | Macro-F1 | -| -------------------------- | -------------: | --------: | -| ResNet-18 | 75.9 | 0.754 | -| ResNet-50 | 79.7 | 0.799 | -| ResNet-152 | 80.5 | 0.804 | -| ViT-B/16 | 79.0 | 0.790 | -| DeiT-B | 80.3 | 0.802 | -| EfficientNet-B4 | **84.9** | **0.849** | -| CLIP ViT-L/14 (zero-shot) | 37.9 | 0.350 | -| CLIP ViT-L/14 (fine-tuned) | 69.1 | 0.675 | - -> CLIP zero-shot performs poorly because HUSIC labels such as *activated exterior space* and *non-activated interior space* are not standard web image categories. Fine-tuning substantially improves CLIP, but it remains below supervised classifiers. The interior-without-people vs. interior-with-people boundary is the most challenging distinction across all models. - ---- - -## Task 2: Cross-Modal Image–Text Retrieval - -**Goal:** Given a text query, retrieve matching images (text-to-image), or given an image, retrieve matching text (image-to-text). - -**File format:** Three Excel spreadsheets (`train.xlsx`, `val.xlsx`, `test.xlsx`) under `02 Text-Image Pairs/`. Each row describes one image and its associated Weibo post metadata. - -### Metadata Schema -To maximise privacy protection and minimise re-identification risk, the public release contains only three essential columns: - -| Column | Type | Description | Task 2 Role | -| --------------------- | ------- | ------------------------------------------------------------ | ------------------------------------------------------ | -| `Image Label` | string | HUSIC class label (e.g., `Exterior urban spaces with people`) | **T2-A query text** (category-level retrieval) | -| `Image Filename` | string | Unique encrypted identifier | **Primary join key** linking spreadsheet to image file | -| `Post Text` | string | Original Weibo post text (Chinese, unmodified) | **T2-B query text** (post-level retrieval) | - -All other fields, including encrypted User IDs, Post IDs, timestamps, City Name, Place Tag, Posting Tool, Mentioned Users, Extracted Topics/Locations, and all engagement metrics (likes, reposts, comments) β€” have been completely removed from the public release to ensure maximum privacy protection. The remaining three columns provide the minimum information necessary to support the three benchmark tasks while making re-identification practically impossible. - -> **Note:** The released dataset retains **original Chinese text** in the `Post Text` column to preserve linguistic authenticity and avoid translation distortion, which is scientifically important for Task 2 evaluation. English text in this README is for illustrative purposes only. - -### Two Retrieval Sub-Tasks - -Urban-ImageNet supports two complementary retrieval configurations that reflect increasing real-world difficulty: - -| Sub-task | Query Text Source | Ground Truth | Difficulty | Notes | -| ------------------------ | ------------------------------------------------------------ | ----------------------------------------------------- | ---------- | ------------------------------------------------------------ | -| **T2-A: Category-level** | `Image Label` column β€” HUSIC class name/definition (e.g., `Exterior urban spaces with people`) | All images sharing the same `Image Label` | Moderate | Structured semantic alignment; good for zero-shot transfer evaluation | -| **T2-B: Post-level** | `Post Text` column β€” original Weibo post narrative | All images attached to the same post (up to 9 images) | Hard | Informal colloquial language; loose image–text coupling; multi-positive ground truth | - -**Bidirectional use:** Both sub-tasks support either direction: - -- **Image β†’ Text**: given an image, retrieve its HUSIC label or matching post text. -- **Text β†’ Image**: given a HUSIC label or post text, retrieve the matching image(s). For T2-B, one post may correspond to up to **9 images**, so evaluation must use a multi-positive retrieval protocol rather than assuming one-to-one caption–image correspondence. - -### T2 Baseline Results (10K test set) - -| Setting | Model | R@1 | R@5 | R@10 | mAP | MedR | -| ----------------------- | ----------------- | -------: | -------: | --------: | -------: | ------: | -| **T2-A Category label** | CLIP (zero-shot) | 54.2 | 96.5 | 100.0 | 53.3 | 1.5 | -| | CLIP (fine-tuned) | 92.7 | 99.8 | 100.0 | 90.7 | 1.0 | -| | BLIP (zero-shot) | 14.9 | 43.6 | 80.0 | 19.8 | 6.2 | -| | BLIP (fine-tuned) | **94.2** | **99.8** | **100.0** | **93.3** | **1.0** | -| **T2-B Post text** | CLIP (zero-shot) | 2.6 | 5.4 | 7.0 | 4.5 | 328 | -| | CLIP (fine-tuned) | **8.1** | **16.9** | **23.5** | **13.2** | **64** | -| | BLIP (zero-shot) | 0.1 | 0.4 | 1.2 | 0.8 | 477 | -| | BLIP (fine-tuned) | 1.9 | 6.8 | 11.6 | 5.5 | 92 | -| **T2-B Post + label** | CLIP (fine-tuned) | **9.3** | **22.8** | **32.3** | **17.0** | **25** | - -> Category-label retrieval is near-trivial after fine-tuning (β‰₯92% R@1), confirming that HUSIC descriptions provide strong cross-modal signal. Post-text retrieval is substantially harder: Weibo posts are short informal narratives (median 32 characters) rather than image descriptions, and a single post may accompany images spanning multiple HUSIC classes. Against a random-chance baseline of ~0.1% R@1, fine-tuned CLIP achieves 8.1% (76Γ— chance), establishing a concrete baseline for future urban-domain vision–language models. +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 -![T2 Retrieval Results](Figures/03-Task2_Results.jpg) -*Figure 3: T2 retrieval results (avg. T2I + I2T). Category-label retrieval (left) is near-trivial after fine-tuning; post-text retrieval (right) remains genuinely challenging, establishing an important open problem for urban-domain vision–language research.* +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`. ---- - -## Task 3: Instance Segmentation +For example: -**Goal:** Detect and delineate urban-domain objects within each image using pixel-level instance masks. - -**File format:** Three COCO-compatible JSON files (`train.json`, `val.json`, `test.json`) under `03 Instance Segmentation/`. - -### Annotation JSON Structure - -Each JSON file follows the COCO format with the following fields: - -```json -{ - "info": { - "description": "Urban-ImageNet Instance Segmentation Annotations", - "split": "train", - "version": "1.0" - }, - "categories": [ {"id": 0, "name": "Exterior urban spaces with people"}, ... ], - "images": [ - { - "id": 0, - "file_name": "2668383_2020-01-21_0.jpg", - "width": 512, - "height": 384, - "classification_label": 0 - }, ... - ], - "annotations": [ - { - "id": 0, - "image_id": 0, - "category_id": 0, - "detected_label": "person", - "detection_score": 0.8732, - "bbox": [x, y, width, height], - "area": 4512, - "segmentation": { "counts": "...", "size": [384, 512] }, - "iscrowd": 0 - }, ... - ] -} +```text +Image Filename = 2668383_2020-01-21_0 +Image file = 2668383_2020-01-21_0.jpg ``` -**Extended fields beyond standard COCO:** - -- `classification_label` (in `images`): the HUSIC class ID of the image β€” enables multi-task joint training and evaluation. -- `detected_label` (in `annotations`): the specific object term detected by Grounding DINO (e.g., `"person"`, `"retail shelf"`, `"escalator"`). -- `detection_score` (in `annotations`): Grounding DINO confidence score, enabling downstream threshold-based filtering. -- Segmentation masks are stored in **COCO RLE format** (run-length encoding), directly compatible with `pycocotools`. - -### Annotation Pipeline - -Annotations were generated using a two-stage automatic pipeline followed by human quality control: - -1. **Grounding DINO** (text-prompted open-vocabulary object detection) identifies bounding boxes using class-specific vocabulary prompts. - -2. **SAM 2** (Segment Anything Model 2) refines each detected box into a pixel-level instance mask. - -3. **NMS** (Non-Maximum Suppression) removes overlapping detections. - -4. **Area filtering** removes very small (noise) and very large (full-image) detections. - -5. **Human review** was applied to the evaluation subset, with stricter confidence thresholds (β‰₯0.50 detection score, β‰₯0.88 IoU), ensuring reliable ground truth for model comparison. - - Training pseudo-labels use more permissive thresholds (β‰₯0.35, β‰₯0.80). Users should account for the pseudo-label nature of annotations when interpreting segmentation performance. - -### Per-Class Segmentation Vocabulary - -Each HUSIC class uses a tailored vocabulary of 12–20 object terms designed to capture the semantically appropriate instances for that scene type, maximising detection recall while minimising false positives. - -| ID | Class | Segmentation Object Terms | -| ---: | ------------------------------------ | ------------------------------------------------------------ | -| 0 | Exterior urban spaces with people | person Β· crowd Β· pedestrian Β· building faΓ§ade Β· lawn Β· street lamp Β· glass curtain wall Β· sky Β· tree Β· shrub Β· fence Β· road Β· water Β· river Β· vehicle Β· sculpture Β· installation Β· pavement Β· street signage Β· fountain | -| 1 | Exterior urban spaces without people | building faΓ§ade Β· glass curtain wall Β· wooden faΓ§ade Β· tree Β· shrub Β· lawn Β· sky Β· pavement Β· road Β· water Β· river Β· lantern Β· sculpture Β· installation Β· street lamp Β· signage Β· fence Β· bridge Β· water feature Β· fountain | -| 2 | Interior urban spaces with people | person Β· shopper Β· crowd Β· retail shelf Β· escalator Β· elevator Β· ceiling Β· floor tile Β· glass partition Β· display case Β· door Β· indoor plant Β· wall Β· window Β· handrail Β· column | -| 3 | Interior urban spaces without people | retail shelf Β· escalator Β· indoor corridor Β· ceiling Β· floor tile Β· marble floor Β· glass partition Β· display case Β· wall Β· column Β· indoor plant Β· elevator Β· door Β· window Β· lighting fixture Β· handrail | -| 4 | Hotel or commercial lodging spaces | hotel bed Β· furniture Β· sofa Β· carpet Β· marble floor Β· tile floor Β· wooden floor Β· ceiling Β· bathroom Β· window Β· curtain Β· lamp | -| 5 | Private home interiors | sofa Β· bed Β· dining table Β· floor Β· ceiling Β· kitchen Β· bookshelf Β· wardrobe Β· window Β· lamp Β· carpet Β· wall | -| 6 | Food or drink items | food dish Β· meal plate Β· dessert Β· beverage cup Β· coffee Β· drink bottle Β· bowl Β· chopsticks Β· spoon Β· dining table Β· person Β· restaurant interior | -| 7 | Retail products and merchandise | fashion clothing Β· shoes Β· cosmetics Β· product package Β· merchandise Β· retail shelf Β· bag Β· jewelry Β· electronics Β· store window Β· mannequin Β· person | -| 8 | Human-centered portrait | person Β· face Β· building faΓ§ade Β· sky Β· tree Β· floor Β· food Β· animal Β· vehicle Β· indoor background | -| 9 | Other non-spatial content | animal Β· person Β· vehicle Β· advertisement poster Β· text Β· QR code Β· screenshot Β· sculpture Β· meme Β· sky Β· plant Β· signage Β· graphic design Β· logo Β· map Β· infographic Β· chat record | - -> **T3 scope note:** Instance segmentation masks are generated for all 10 HUSIC classes. The T3 evaluation benchmark adopts a **class-agnostic protocol** β€” treating all detected objects as a single `object` category β€” to produce conservative, architecture-comparable metrics uncorrupted by class-imbalanced pseudo-labels. Per-class AP results are available in the supplementary material of the paper. - -### T3 Baseline Results (quality-filtered evaluation subset, confidence β‰₯ 0.50, IoU β‰₯ 0.88) - -| Model | AP | APβ‚…β‚€ | AP₇₅ | mIoU | FPS | -| ------------------------ | --------: | --------: | --------: | ----: | ---: | -| Mask R-CNN | 0.267 | 0.472 | 0.276 | 0.629 | 15.4 | -| Cascade Mask R-CNN | 0.290 | 0.495 | 0.299 | 0.635 | 12.7 | -| **Mask R-CNN + SAM** | **0.373** | 0.563 | **0.378** | β€” | ~0 | -| Cascade Mask R-CNN + SAM | 0.369 | **0.531** | 0.380 | β€” | ~0 | -| GT-box SAM (oracle†) | 0.749 | 0.924 | 0.805 | β€” | β€” | - -> †GT-box SAM uses ground-truth bounding boxes as prompts β€” an oracle upper bound, not a trainable baseline. Adding SAM box-refinement to Mask R-CNN increases AP by ~40% relative (0.267 β†’ 0.373), establishing a strong open-source baseline for future work. The gap between trainable models and the oracle (0.373 vs. 0.749) highlights substantial room for improvement in urban commercial-space instance segmentation. - - -![T3 Qualitative Segmentation Examples](Figures/04-Task3_Examples.jpg) -*Figure 4: Task 3 qualitative segmentation examples across HUSIC classes. Colour-coded instance masks from Mask R-CNN, Cascade Mask R-CNN, and Mask R-CNN+SAM. The domain-specific vocabulary enables detection of urban-specific objects (escalators, retail shelves, display cases, street lamps) not well-covered by general segmentation benchmarks.* - ---- - -## Urban-ImageNet-lib - - -![Urban-ImageNet-lib Architecture](Figures/05-Benchmark.jpg) -*Figure 5: Urban-ImageNet-lib architecture β€” a unified benchmarking framework supporting all three tasks with standardised cross-dataset comparison adapters.* - -**Urban-ImageNet-lib** is a Python benchmarking library providing: - -- Modular data loaders for all three tasks and all four dataset tiers. -- Standard fine-tuning pipelines for T1 (classification), T2 (retrieval), and T3 (segmentation) baselines. -- Evaluation scripts with metrics matching established benchmarks (T1 ↔ Places365/SUN; T2 ↔ MS-COCO Captions/Flickr30K; T3 ↔ MS-COCO Instance Seg./Cityscapes). -- Cross-dataset adapters enabling direct performance comparison in a unified table. - - -See the [GitHub repository](https://github.com/yiasun/dataset-2) for full installation instructions and usage examples. - ---- - -## Scaling Behaviour - -Urban-ImageNet's four-tier design enables systematic study of how classification accuracy and computational cost scale with dataset size. All balanced tiers (1K / 10K / 100K) are strictly class-balanced so that performance differences across tiers are attributable to data quantity alone, without confounding from class imbalance. All models were trained separately on each tier and evaluated on a **shared held-out 10K test set**. - -### T1 Scaling: Top-1 Accuracy and Macro-F1 - -| Model | 1K Acc. (%) | 1K F1 | 10K Acc. (%) | 10K F1 | 100K Acc. (%) | 100K F1 | -|-------|------------:|------:|-------------:|-------:|--------------:|--------:| -| ResNet-50 | 66.5 | 0.661 | 78.1 | 0.781 | 83.5 | 0.835 | -| ResNet-152 | 67.3 | 0.670 | 79.0 | 0.787 | 83.5 | 0.834 | -| CLIP (fine-tuned) | 70.8 | 0.708 | 78.0 | 0.780 | 82.3 | 0.822 | -| LLaVA-1.5 (fine-tuned) | 76.8 | 0.767 | 81.2 | 0.812 | β€” † | β€” † | - -> † LLaVA-1.5 100K fine-tuning was not completed due to computational constraints (~3,200Γ— slower per sample than ResNet-50; estimated >150 GPU-hours on H100). - -All models improve monotonically with scale. The **1Kβ†’10K gain (10–12%) consistently exceeds the 10Kβ†’100K gain (5%)**, consistent with standard scaling laws. LLaVA-1.5's stronger language-grounded priors give it an advantage at small scales (76.8% at 1K vs. 66.5–70.8% for others) but it is computationally prohibitive at 100K. - -### Hierarchical T1 Scaling: Coarser Distinctions Are Easier +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. -HUSIC's hierarchical structure means models can be evaluated at three levels of granularity. At 100K, models substantially exceed their 10-class accuracy when evaluated on coarser distinctions: +### Text-Image Pair Columns -| Model | Tier | Spatial/Non-spatial Acc. | Exterior/Interior Acc. | 10-class Acc. | -|-------|------|-------------------------:|-----------------------:|--------------:| -| ResNet-50 | 1K | 88.7% | 86.7% | 66.5% | -| ResNet-50 | 10K | 92.5% | 92.3% | 78.1% | -| ResNet-50 | 100K | 93.9% | **95.0%** | 83.5% | -| ResNet-152 | 100K | 94.2% | 94.7% | 83.5% | -| CLIP (FT) | 100K | 94.0% | 87.5% | 82.3% | -| LLaVA-1.5 (FT) | 10K | 91.9% | 85.4% | 81.2% | +| 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 | -At 100K, spatial vs. non-spatial binary accuracy reaches 94% and exterior vs. interior reaches 95%, confirming that HUSIC captures semantically meaningful hierarchical structure. The gap between coarse (94–95%) and fine-grained (83–85%) accuracy highlights that the activation-level distinctions (e.g., *with people* vs. *without people*) remain the hardest sub-problems. +### T2 Evaluation Settings -### T2-Post Retrieval Scaling +Urban-ImageNet supports two image-text matching settings: -Post-level retrieval difficulty grows naturally as the candidate gallery expands. Fine-tuned CLIP's average R@1 drops from **39.5%** on the 1K split (100-image pool) to **8.1%** on the 10K split (1,000-image pool), confirming that T2-B is a scalably challenging benchmark. +| 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 | -| Model | 1K split β€” Avg. R@1 (%) | 1K split β€” Avg. mAP | 10K split β€” Avg. R@1 (%) | 10K split β€” Avg. mAP | -|-------|------------------------:|--------------------:|-------------------------:|---------------------:| -| CLIP (fine-tuned) | 39.5 | 0.501 | 8.1 | 0.132 | -| BLIP-2 (fine-tuned) | 28.1 | 0.392 | 5.0 | 0.094 | -| BLIP (fine-tuned) | 16.6 | 0.283 | 1.9 | 0.055 | +Task 2 can be used in either direction: -*(Avg. = average of T2I and I2T directions; mAP as a fraction 0–1.)* +- 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. ---- - -## Data Collection and Construction Pipeline - - -![Dataset Construction Pipeline](Figures/06-Data-Collection.png) -*Figure 6: Overview of the Urban-ImageNet dataset construction and annotation pipeline β€” from Weibo crawling through privacy processing, HUSIC annotation, and multi-task organisation.* - -Urban-ImageNet was constructed through a five-stage pipeline: - -1. **Collection** β€” A Python-based web crawler systematically retrieved all public Weibo posts from location-specific hashtags at 61 major urban commercial sites across 24 Chinese cities, covering 2019–2025. Up to 9 image attachments, post text, and metadata were captured per post, yielding a raw corpus of over **4 TB** and **2 million** image–text pairs. +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. -2. **Cleaning** β€” Four-stage deduplication and filtering: (i) near-duplicate removal via perceptual hashing (pHash, Hamming distance ≀ 8); (ii) discard of images smaller than 256Γ—256 px; (iii) NSFW filtering via pre-trained classifier; (iv) removal of systematically repeated commercial advertisement posts via post-text hash similarity. - -3. **Privacy Protection** β€” Automated face detection, licence-plate recognition, and QR-code detection were applied to all images with all detected regions blurred. Original usernames were stripped and replaced with opaque numerical identifiers. Images were resized to a maximum side length of **512 px**. The raw 4 TB corpus is retained securely by the authors and will not be publicly released. - -4. **HUSIC Annotation (T1 & T2)** β€” The 100K balanced benchmark set was manually annotated by three trained researchers following a standardised guideline. A shared 3,000-image double-annotation subset yielded **Cohen's ΞΊ = 0.87** (near-perfect agreement). Disagreements were resolved by majority vote and guideline revision. The annotation process took approximately **two years** of sustained effort. - -5. **Instance Segmentation (T3)** β€” Pseudo-labels were generated using Grounding DINO + SAM 2 with per-class vocabulary prompts, followed by NMS and area filtering. The evaluation subset was reviewed with stricter thresholds and human spot-checks. - ---- - -## Geographic and Site Coverage +## Task 3: Instance Segmentation -Urban-ImageNet covers **61 urban commercial sites** across **24 Chinese cities** spanning **8 macro-regions**, including all four first-tier cities (Beijing, Shanghai, Guangzhou, Shenzhen), leading new first-tier cities (Chengdu, Hangzhou, Nanjing, Wuhan, Xi'an, Chongqing), and a range of second-tier regional centres. Sites span three spatial typologies: enclosed malls, open-air pedestrian precincts, and mixed-typology developments. The full site list is provided in the [paper appendix](https://arxiv.org/abs/2605.09936). +Task 3 uses the `03 Instance Segmentation` JSON files. The format is COCO-style and includes: - -![Geographic Distribution of Urban-ImageNet Collection Cities](Figures/07-Geo-distribution.png) -*Figure 7: Geographic distribution of Urban-ImageNet's 24 collection cities. Marker size is proportional to the number of collected image–text pairs per city; colour encodes macro-region.* +- `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** β€” posts whose visibility was explicitly set to "open to all" by the account holder at the time of collection. Although source posts were public, the released dataset applies **multiple layers of privacy protection** in line with the practice of large-scale street-level datasets (e.g., Google Street View): - -| Protection Measure | Implementation | -| ------------------------- | ------------------------------------------------------------ | -| **Data minimisation** | Metadata contains only **3 columns** (`Image Label`, `Image Filename`, `Post Text`); all non-essential fields removed | -| **Full-body blurring** | Automated detection + **full-person / full-vehicle blurring** (not just faces/licence plates); clothing, body shape, hairstyle, tattoos, and vehicle styling are all obscured | -| **Image resolution cap** | Released at ≀ 512 px maximum side length; original-resolution corpus (4 TB) **not publicly released** | -| **PII text scrubbing** | `Post Text` undergoes automated PII detection; names, phone numbers, and IDs are masked as "***" | -| **No re-identification pointers** | No original post URLs, account links, profile pictures, or usernames included | -| **Manual quality audit** | Second-pass manual spot-check to verify blurring completeness | -| **Search-based linkage tests** | Empirically verified that the three-column release cannot be linked back to live Weibo pages or identifiable users | - -**Data-use agreement:** Researchers accessing Urban-ImageNet must agree to a data-use agreement restricting use to **non-commercial academic research** and prohibiting: - -- Re-identification of individuals - -- Facial recognition or biometric profiling - -- Account or identity reconstruction - -- Surveillance or social scoring +Urban-ImageNet is derived from public Weibo posts. Although the source posts were public, the release uses privacy-protected derivatives: -- Law-enforcement targeting +- 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. -- Commercial profiling or demographic inference +The dataset is intended for non-commercial academic research in urban perception, computational urban studies, multimodal learning, image classification, image-text retrieval, and segmentation. -**Research purpose:** Urban-ImageNet is designed to advance evidence-based urban design and planning through improved AI perception of public spaces β€” serving a clear public good. The authors will monitor dataset use and reserve the right to retract access in cases of misuse. +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 Known Biases - -- **Geographic bias:** The corpus is entirely China-sourced and should not be treated as globally representative of urban commercial spaces. -- **Platform bias:** Weibo users are not representative of all city residents; the dataset over-represents younger, urban, mobile-connected demographics. -- **Visual selection bias:** Social media images over-represent photogenic, popular, and personally meaningful scenes; empty or mundane spaces are systematically underrepresented. -- **Linguistic bias:** Post text is original Chinese social-media language containing slang, emoji, hashtags, and frequently loose image–text coupling. -- **Class imbalance in 2M corpus:** The full corpus reflects natural posting frequencies and is significantly class-imbalanced; the balanced 1K/10K/100K tiers do not reflect natural class distributions. -- **T3 pseudo-labels:** Task 3 annotations are model-generated pseudo-labels (Grounding DINO + SAM 2), not exhaustive human pixel-level labels; users should account for this when training or evaluating segmentation models. -- **Temporal scope:** Posts span 2019–2025; urban commercial environments evolve over time and some sites may have changed significantly. - ---- - -## Related Work - -Urban-ImageNet is designed as a **domain-specific complement** to the following general-purpose benchmarks: - -| Benchmark | Task Covered | Relation to Urban-ImageNet | -| ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ | -| [Places365](http://places2.csail.mit.edu/) | Scene classification | Urban-ImageNet provides theory-grounded, activation-aware sub-categories of Places365 classes | -| [SUN Database](https://3dvision.princeton.edu/projects/2010/SUN/) | Scene classification | Complementary focus on commercial urban spaces with social context | -| [MS-COCO Captions](https://cocodataset.org/) | Image–text retrieval | Urban-ImageNet provides authentic first-person social media narratives vs. COCO's objective third-person captions | -| [Flickr30K](http://shannon.cs.illinois.edu/DenotationGraph/) | Image–text retrieval | Urban-ImageNet provides Chinese-language, domain-specific, multi-positive retrieval ground truth | -| [MS-COCO Instance Seg.](https://cocodataset.org/) | Instance segmentation | Urban-ImageNet provides domain-specific commercial-space vocabulary (retail shelves, escalators, hotel beds, etc.) | -| [Cityscapes](https://www.cityscapes-dataset.com/) | Semantic/instance segmentation | Urban-ImageNet focuses on commercial interior and mixed exterior spaces vs. Cityscapes' driving-scene focus | - ---- +## 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 -If you use Urban-ImageNet in your research, please cite our paper: - ```bibtex -@article{ou2026urbanimagenet, - title = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception}, - author = {Ou, Yiwei and Cheung, Chung Ching and Ang, Jun Yang and Ren, Xiaobin and Sun, Ronggui and Gao, Guansong and Zhao, Kaiqi and Manfredini, Manfredo}, - journal = {arXiv preprint arXiv:2605.09936}, - year = {2026}, - eprint = {2605.09936}, - archivePrefix = {arXiv}, - primaryClass = {cs.CV}, - url = {https://arxiv.org/abs/2605.09936} +@misc{urbanimagenet2026, + title = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception}, + author = {Urban-ImageNet Research Team}, + year = {2026}, + note = {Dataset and benchmark for NeurIPS 2026 Evaluations and Datasets Track} } -``` - -**Paper:** [arXiv:2605.09936](https://arxiv.org/abs/2605.09936) -**Dataset:** [huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet](https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet) -**Benchmark code:** [github.com/yiasun/dataset-2](https://github.com/yiasun/dataset-2) - ---- - -## License - -The dataset is released under **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)**. - -You are free to use, share, and adapt this dataset for **non-commercial academic research** with appropriate attribution, provided that you give appropriate credit and distribute any derivative works under the same license. Commercial use of any kind is prohibited. - -See [LICENSE](https://creativecommons.org/licenses/by-nc-sa/4.0/) for full terms. +``` \ No newline at end of file diff --git "a/Sample Dataset/01 Images with labels/Exterior urban spaces with people/1197195715_2023\345\271\26412\346\234\21015\346\227\245_5.jpg" "b/Sample Dataset/01 Images with labels/Exterior urban spaces with people/1197195715_2023\345\271\26412\346\234\21015\346\227\245_5.jpg" deleted file mode 100644 index acfa0f51ab1d705146401672e65c5811ffa856f0..0000000000000000000000000000000000000000 --- "a/Sample Dataset/01 Images with labels/Exterior urban spaces with people/1197195715_2023\345\271\26412\346\234\21015\346\227\245_5.jpg" +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d87500ae5e3c8e2e30d426c7a44f7da4d194d582b94f535a070f461ba7bf7def -size 115016 diff --git "a/Sample Dataset/01 Images with labels/Exterior urban spaces with 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cc-by-nc-sa-4.0 -pretty_name: Urban-ImageNet β€” Sample Dataset -task_categories: - - image-classification - - image-to-text - - text-to-image - - zero-shot-image-classification - - image-segmentation -modalities: - - image - - text -language: - - zh - - en -size_categories: - - n<1K -tags: - - urban-perception - - social-media - - weibo - - image-text-retrieval - - instance-segmentation - - computational-urban-studies - - urban-ai - - chinese-cities - - husic - - cross-modal-retrieval - - multi-modal - - scene-classification - - urban-space-perception - - sample-dataset ---- - - -# πŸ™οΈ Urban-ImageNet β€” Sample Dataset - -**A balanced 100-image quality-inspection sample drawn from the Urban-ImageNet 100K benchmark, covering all three annotation tasks.** - - - -> This repository is a **compact, self-contained sample** of Urban-ImageNet, designed to let anyone quickly inspect data quality, annotation completeness, and label fidelity without downloading the 100K large corpus (6+ GB). It faithfully mirrors the structure, format, and annotation conventions of the 100K benchmark across all three tasks. - ---- - -## Overview - -Urban-ImageNet is a large-scale multi-modal dataset for urban space perception (see the [full dataset page](https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet) and [paper](https://arxiv.org/abs/2605.09936) for complete documentation). This sample repository provides: - -- **100 images** β€” 10 HUSIC classes Γ— 10 images per class, drawn from the **training split** of the 100K dataset. -- **Complete Task 1 labels** β€” images stored in `ImageFolder`-compatible class subdirectories. -- **Complete Task 2 annotations** β€” the corresponding 100 rows from the 100K text-image pairs file, with all metadata columns preserved. -- **Complete Task 3 annotations** β€” a filtered COCO-format JSON containing all instance segmentation masks for the 100 images. -- **Instance mask visualisations** β€” side-by-side original / coloured-mask / legend images for every one of the 100 images. - -This sample represents approximately **0.1%** of the 100K benchmark by image count and is strictly class-balanced, making it representative of the 100K dataset's overall structure and label distribution. - ---- - -## How the Sample Was Created - -The sample was constructed through the following transparent, reproducible procedure: - -1. **Source pool:** The **training split** of the Urban-ImageNet 100K balanced benchmark (80,000 images; 8,000 per HUSIC class). - -2. **Stratified random sampling:** For each of the 10 HUSIC classes, **10 images were drawn uniformly at random** without replacement, independently per class, guaranteeing strict class balance (10 images Γ— 10 classes = 100 images total). - -3. **Privacy verification:** All selected images had already undergone automated face blurring, licence-plate blurring, and QR-code blurring as part of the full dataset pipeline. The 100 selected images were additionally subject to **manual human inspection** to confirm that all privacy-sensitive regions are fully obscured before inclusion in this sample. - -4. **Annotation extraction:** - - **T1:** Images placed into their HUSIC class subdirectory β€” no additional annotation step required. - - **T2:** The 100 `Image Filename` values were used to filter rows from the 100K `train.xlsx` file. All original metadata columns are preserved without modification. - - **T3:** The 100 image filenames were used to filter entries from the 100K COCO annotation file, extracting all matching `images` entries and their associated `annotations`. Image IDs and annotation IDs are re-indexed sequentially within the sample file. - -5. **Mask visualisation generation:** For each of the 100 images, a side-by-side visualisation was rendered using a standard pipeline. Each visualisation shows: left panel β€” original image; right panel β€” instance mask overlay with per-instance colour coding; right margin β€” colour-coded legend of detected object labels. - ---- - -## File Structure - -``` -Sample Dataset/ -β”‚ -β”œβ”€β”€ 01 Images with labels/ ← Task 1: Scene Classification -β”‚ β”œβ”€β”€ Exterior urban spaces with people/ (10 images) -β”‚ β”œβ”€β”€ Exterior urban spaces without people/ (10 images) -β”‚ β”œβ”€β”€ Food or drink items/ (10 images) -β”‚ β”œβ”€β”€ Hotel or commercial lodging spaces/ (10 images) -β”‚ β”œβ”€β”€ Human-centered portrait/ (10 images) -β”‚ β”œβ”€β”€ Interior urban spaces with people/ (10 images) -β”‚ β”œβ”€β”€ Interior urban spaces without people/ (10 images) -β”‚ β”œβ”€β”€ Other non-spatial content/ (10 images) -β”‚ β”œβ”€β”€ Private home interiors/ (10 images) -β”‚ └── Retail products and merchandise/ (10 images) -β”‚ ──────────────────── -β”‚ Total: 100 images -β”‚ -β”œβ”€β”€ 02 Text-Image Pairs/ ← Task 2: Cross-Modal Retrieval -β”‚ └── Sample Dataset Paired Texts.xlsx -β”‚ -└── 03 Instance Segmentation/ ← Task 3: Instance Segmentation - β”œβ”€β”€ Sample Dataset Annotations.json - └── Visualization of instance labels/ - β”œβ”€β”€ Exterior urban spaces with people/ (10 visualisations) - β”œβ”€β”€ Exterior urban spaces without people/ (10 visualisations) - β”œβ”€β”€ Food or drink items/ (10 visualisations) - β”œβ”€β”€ Hotel or commercial lodging spaces/ (10 visualisations) - β”œβ”€β”€ Human-centered portrait/ (10 visualisations) - β”œβ”€β”€ Interior urban spaces with people/ (10 visualisations) - β”œβ”€β”€ Interior urban spaces without people/ (10 visualisations) - β”œβ”€β”€ Other non-spatial content/ (10 visualisations) - β”œβ”€β”€ Private home interiors/ (10 visualisations) - └── Retail products and merchandise/ (10 visualisations) -``` - -> **Image format:** All images are JPEG, privacy-protected (faces, licence plates, and QR codes blurred), resized to a maximum long edge of 512 px. - ---- - -## Correspondence Between Files - -All three annotation modalities share a common **image filename key** in the format `{UserID}_{PostTime}_{Index}` (e.g., `1197195715_2023εΉ΄12月15ζ—₯_5`). The table below shows how each component references this key: - -| Component | Key field | Example value | -|-----------|-----------|---------------| -| `01 Images with labels/{class}/` | Filename stem (without `.jpg`) | `1197195715_2023εΉ΄12月15ζ—₯_5.jpg` | -| `Sample Dataset Paired Texts.xlsx` | `Image Filename` column | `1197195715_2023εΉ΄12月15ζ—₯_5` | -| `Sample Dataset Annotations.json` | `images[].file_name` | `1197195715_2023εΉ΄12月15ζ—₯_5.jpg` | -| `Visualization of instance labels/{class}/` | Filename stem (without `.jpg`) | `1197195715_2023εΉ΄12月15ζ—₯_5.jpg` | - ---- - -## Task 1: Scene Classification Labels - -Images are stored in an `ImageFolder`-compatible layout. The subdirectory name is the ground-truth HUSIC label, directly loadable with standard PyTorch or TensorFlow pipelines: - -```python -from torchvision.datasets import ImageFolder - -dataset = ImageFolder(root="Sample Dataset/01 Images with labels") -# dataset.classes β†’ ['Exterior urban spaces with people', ...] -# dataset.class_to_idx β†’ {'Exterior urban spaces with people': 0, ...} -print(len(dataset)) # 100 -``` - -The 10 HUSIC classes (integer IDs assigned by `ImageFolder`'s lexicographic sort): - -| Class ID | Class Label | -|---------:|-------------| -| 0 | Exterior urban spaces with people | -| 1 | Exterior urban spaces without people | -| 2 | Food or drink items | -| 3 | Hotel or commercial lodging spaces | -| 4 | Human-centered portrait | -| 5 | Interior urban spaces with people | -| 6 | Interior urban spaces without people | -| 7 | Other non-spatial content | -| 8 | Private home interiors | -| 9 | Retail products and merchandise | - -> **Note:** In the full Urban-ImageNet dataset, HUSIC class IDs 0–9 follow the theoretical ordering (exterior-before-interior, spatial-before-non-spatial). When loading with `ImageFolder`, always use `class_to_idx` to map label strings to IDs rather than assuming a fixed integer correspondence. - ---- - -## Task 2: Text-Image Pair Metadata - -`Sample Dataset Paired Texts.xlsx` contains **100 rows** β€” one per image β€” with all metadata columns from the original 100K dataset preserved without modification. - -### Column Schema - -| Column | Type | Description | -|--------|------|-------------| -| `Image Label` | string | HUSIC class label | -| `Image Filename` | string | Join key linking to image file and T3 annotation | -| `Post ID` | integer | Anonymised numerical post identifier | -| `User ID` | integer | Anonymised numerical user identifier | -| `Post Time` | string | Original post timestamp | -| `Post Text` | string | Original Weibo post text (Chinese, unmodified) | -| `City` | string | City of the location tag | -| `Place Tag` | string | Location hashtag / commercial-site place tag | -| `Posting Tool` | string | Client or posting-source string | -| `Mentioned Users` | string | Anonymised or empty mentioned-user field | -| `Extracted Topics` | string | Topic / hashtag terms extracted from post text | -| `Extracted Locations` | string | Location mentions extracted from post text | -| `Like Count` | integer | Public engagement count at collection time | -| `Repost Count` | integer | Public repost count at collection time | -| `Comment Count` | integer | Public comment count at collection time | - -> `Post Text` retains original Chinese to preserve linguistic authenticity for Task 2 evaluation. All columns retain the original data types and formatting from the 100K source file. - ---- - -## Task 3: Instance Segmentation Annotations - -`Sample Dataset Annotations.json` uses the same extended COCO format as the full Urban-ImageNet dataset. - -### JSON Structure - -```json -{ - "info": { - "description": "Urban-ImageNet Instance Segmentation Annotations", - "split": "train", - "version": "1.0", - "annotation_tool": "Grounding DINO + SAM2" - }, - "categories": [ - {"id": 0, "name": "Exterior urban spaces with people"}, - ... - ], - "images": [ - { - "id": 0, - "file_name": "1197195715_2023εΉ΄12月15ζ—₯_5.jpg", - "width": 512, - "height": 384, - "classification_label": 0 - } - ], - "annotations": [ - { - "id": 0, - "image_id": 0, - "category_id": 0, - "detected_label": "person", - "detection_score": 0.8732, - "bbox": [x, y, width, height], - "area": 4512, - "segmentation": {"counts": "...", "size": [384, 512]}, - "iscrowd": 0 - } - ] -} -``` - -**Extended fields beyond standard COCO:** - -- `classification_label` *(in `images`)*: HUSIC class ID β€” enables multi-task joint training. -- `detected_label` *(in `annotations`)*: the specific object term detected by Grounding DINO (e.g., `"escalator"`, `"retail shelf"`, `"hotel bed"`). -- `detection_score` *(in `annotations`)*: Grounding DINO confidence score, enabling threshold-based downstream filtering. -- Segmentation masks are stored in **COCO RLE format**, directly compatible with `pycocotools`. - -### Instance Mask Visualisations - -The `Visualization of instance labels/` subfolder provides a visual verification of every annotation in this sample. Each visualisation image shows: - -- **Left half:** the original image. -- **Right half:** the same image with coloured per-instance mask overlays (each unique instance receives a distinct colour). -- **Right margin:** a colour-coded legend listing the `detected_label` for each coloured mask. - -Visualisation filenames match their source images exactly (e.g., `1197195715_2023εΉ΄12月15ζ—₯_5.jpg`), within the same class subdirectory, enabling direct one-to-one correspondence without any filename transformation. - ---- - -## Relationship to the Full Dataset - -| Property | This Sample | 100K Benchmark | -|----------|-------------|----------------| -| Images | 100 | 100,000 | -| Classes | 10 | 10 | -| Images per class | 10 | 10,000 | -| Source split | train only | train / val / test | -| T1 labels | βœ… | βœ… | -| T2 text metadata | βœ… 100 rows | βœ… 80K / 10K / 10K rows | -| T3 instance annotations | βœ… filtered JSON | βœ… train / val / test JSON | -| T3 visualisations | βœ… all 100 images | βœ… selected examples | -| Privacy protection | βœ… automated + manual review | βœ… automated + manual review | -| File format & schema | Identical to full dataset | β€” | - -Code written against this sample runs unchanged on the full dataset by substituting the root path. - ---- - -## Quick-Start - -```python -import json -import pandas as pd -from pathlib import Path -from torchvision.datasets import ImageFolder - -root = Path("Sample Dataset") - -# ── Task 1 ────────────────────────────────────────────────────────────── -dataset = ImageFolder(root=root / "01 Images with labels") -print(dataset.classes) # 10 HUSIC class names -print(len(dataset)) # 100 - -# ── Task 2 ────────────────────────────────────────────────────────────── -df = pd.read_excel(root / "02 Text-Image Pairs" / "Sample Dataset Paired Texts.xlsx") -print(df.shape) # (100, 15) -print(df["Post Text"].iloc[0]) # Chinese post text - -# ── Task 3 ────────────────────────────────────────────────────────────── -with open(root / "03 Instance Segmentation" / "Sample Dataset Annotations.json") as f: - coco = json.load(f) -print(f"Images: {len(coco['images'])}") # 100 -print(f"Annotations: {len(coco['annotations'])}") # typically 500–1500 - -# Join image filename β†’ annotations -img_id_map = {img["file_name"]: img["id"] for img in coco["images"]} -ann_by_img = {} -for ann in coco["annotations"]: - ann_by_img.setdefault(ann["image_id"], []).append(ann) -``` - ---- - -## Privacy and Responsible Use - -All images are derived from **public Weibo posts** and have undergone: - -- Automated face detection and blurring (all detected faces). -- Automated licence-plate detection and blurring. -- Automated QR-code detection and blurring, supplemented by manual spot-checks. -- **Additional manual human review** of all 100 sample images, confirming complete protection of all privacy-sensitive regions. - -Original usernames have been replaced with opaque numerical identifiers. Images are released at ≀ 512 px long edge. Use is restricted to **non-commercial academic research**. Re-identification, biometric profiling, facial recognition, and surveillance applications are strictly prohibited. See the [full dataset page](https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet) for the complete data-use agreement. - ---- - -## Citation - -```bibtex -@article{ou2026urbanimagenet, - title = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception}, - author = {Ou, Yiwei and Cheung, Chung Ching and Ang, Jun Yang and Ren, Xiaobin and Sun, Ronggui and Gao, Guansong and Zhao, Kaiqi and Manfredini, Manfredo}, - journal = {arXiv preprint arXiv:2605.09936}, - year = {2026}, - eprint = {2605.09936}, - archivePrefix = {arXiv}, - primaryClass = {cs.CV}, - url = {https://arxiv.org/abs/2605.09936} -} -``` - -**Paper:** [arXiv:2605.09936](https://arxiv.org/abs/2605.09936) -**Full Dataset:** [huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet](https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet) -**Benchmark code:** [github.com/yiasun/dataset-2](https://github.com/yiasun/dataset-2) - ---- - -## License - -Released under **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)**. -Non-commercial academic research use only. See [LICENSE](https://creativecommons.org/licenses/by-nc-sa/4.0/) for full terms. diff --git a/Urban-ImageNet_Croissant Metadata.json b/Urban ImageNet_Croissant Metadata.json similarity index 100% rename from Urban-ImageNet_Croissant Metadata.json rename to Urban ImageNet_Croissant Metadata.json

- arXiv - GitHub - Full Dataset -