Datasets:

Yiwei-Ou
/

Urban-ImageNet

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+  "name": "Urban-ImageNet",
+  "alternateName": "Urban-ImageNet: A Large-Scale Multi-Modal Dataset for Urban Space Perception Benchmarking",
+  "url": "https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet",
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+  "version": "1.0",
+  "description": "Urban-ImageNet is a large-scale multimodal dataset and benchmark for urban commercial space perception. It contains over 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 10-class taxonomy and supports urban scene classification, category-level and post-level image-text retrieval, instance segmentation, and scaling studies through 1K, 10K, 100K, and full 2M variants.",
+  "dateCreated": "2019-01-01",
+  "datePublished": "2026-05-05",
+  "dateModified": "2026-05-05",
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+    "urban perception",
+    "image classification",
+    "image-text retrieval",
+    "instance segmentation",
+    "social media",
+    "Weibo",
+    "Chinese cities",
+    "urban commercial spaces",
+    "multimodal learning",
+    "computational urban studies"
+  ],
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+      "name": "Urban-ImageNet Research Team"
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+          "husic-classes/class_id": 0,
+          "husic-classes/label": "Exterior urban spaces with people",
+          "husic-classes/group": "Exterior",
+          "husic-classes/definition": "Outdoor urban commercial spaces with visible human presence."
+        },
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+        },
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+          "husic-classes/class_id": 2,
+          "husic-classes/label": "Interior urban spaces with people",
+          "husic-classes/group": "Interior",
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+        },
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+          "husic-classes/group": "Interior",
+          "husic-classes/definition": "Interior commercial spaces focused on spatial design, circulation, fixtures, or displays without people."
+        },
+        {
+          "husic-classes/class_id": 4,
+          "husic-classes/label": "Hotel or commercial lodging spaces",
+          "husic-classes/group": "Accommodation",
+          "husic-classes/definition": "Hotel rooms, serviced apartments, and commercial lodging environments."
+        },
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+          "husic-classes/class_id": 5,
+          "husic-classes/label": "Private home interiors",
+          "husic-classes/group": "Accommodation",
+          "husic-classes/definition": "Private residential interiors connected to the broader commercial-district social-media corpus."
+        },
+        {
+          "husic-classes/class_id": 6,
+          "husic-classes/label": "Food or drink items",
+          "husic-classes/group": "Consumption",
+          "husic-classes/definition": "Food, drinks, dining-table scenes, and restaurant consumption content."
+        },
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+          "husic-classes/class_id": 7,
+          "husic-classes/label": "Retail products and merchandise",
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+          "husic-classes/definition": "Product, merchandise, retail shelf, shopping, and display-window content."
+        },
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+          "husic-classes/class_id": 8,
+          "husic-classes/label": "Human-centered portrait",
+          "husic-classes/group": "Portrait",
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+        },
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+          "husic-classes/label": "Other non-spatial content",
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+          "husic-classes/definition": "Advertisements, screenshots, memes, maps, QR-code-like content, animals, or other non-spatial material."
+        }
+      ]
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+      "description": "Column-level schema for train.xlsx, val.xlsx, test.xlsx, and full-corpus CSV text-image pair files.",
+      "key": {
+        "@id": "t2-columns/column_name"
+      },
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "t2-columns/column_name",
+          "name": "column_name",
+          "description": "Excel/CSV column name.",
+          "dataType": "sc:Text"
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "t2-columns/description",
+          "name": "description",
+          "description": "Column meaning and use.",
+          "dataType": "sc:Text"
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "t2-columns/task_role",
+          "name": "task_role",
+          "description": "Task or metadata role.",
+          "dataType": "sc:Text"
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "t2-columns/example_value",
+          "name": "example_value",
+          "description": "English illustrative example; released Post Text remains original Chinese.",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "t2-columns/column_name": "Image Label",
+          "t2-columns/description": "HUSIC class label for the image; used for category-level image-text alignment and T1 labels.",
+          "t2-columns/task_role": "T1/T2",
+          "t2-columns/example_value": "Exterior urban spaces with people"
+        },
+        {
+          "t2-columns/column_name": "Image Filename",
+          "t2-columns/description": "Image stem used to join spreadsheet rows to image files; follows UserID_PostDate_Index.",
+          "t2-columns/task_role": "Join key",
+          "t2-columns/example_value": "2668383_2020-01-21_0"
+        },
+        {
+          "t2-columns/column_name": "Post ID",
+          "t2-columns/description": "Anonymized numerical post identifier.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "4469639217818281"
+        },
+        {
+          "t2-columns/column_name": "User ID",
+          "t2-columns/description": "Anonymized numerical user identifier; original usernames are not released.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "2668383"
+        },
+        {
+          "t2-columns/column_name": "Post Time",
+          "t2-columns/description": "Original post timestamp in Chinese date-time format.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "2020-01-21 01:00"
+        },
+        {
+          "t2-columns/column_name": "Post Text",
+          "t2-columns/description": "Original Chinese Weibo text; used for post-level image-text retrieval.",
+          "t2-columns/task_role": "T2",
+          "t2-columns/example_value": "Synthetic English illustration: A lively evening walk near a commercial plaza; the released dataset keeps the original Chinese text."
+        },
+        {
+          "t2-columns/column_name": "City",
+          "t2-columns/description": "City associated with the location hashtag or commercial site.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "Chengdu"
+        },
+        {
+          "t2-columns/column_name": "Place Tag",
+          "t2-columns/description": "Location hashtag or place tag used for collection.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "#Chengdu Taikoo Li"
+        },
+        {
+          "t2-columns/column_name": "Posting Tool",
+          "t2-columns/description": "Client or posting-source string after metadata minimization.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "mobile client"
+        },
+        {
+          "t2-columns/column_name": "Mentioned Users",
+          "t2-columns/description": "Anonymized or removed mentioned-user field.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": ""
+        },
+        {
+          "t2-columns/column_name": "Extracted Topics",
+          "t2-columns/description": "Topic or hashtag terms extracted from the post text.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "city life; shopping district"
+        },
+        {
+          "t2-columns/column_name": "Extracted Locations",
+          "t2-columns/description": "Location mentions extracted from the post text.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "Chengdu"
+        },
+        {
+          "t2-columns/column_name": "Like Count",
+          "t2-columns/description": "Public engagement count at collection time.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "52"
+        },
+        {
+          "t2-columns/column_name": "Repost Count",
+          "t2-columns/description": "Public repost count at collection time.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "13"
+        },
+        {
+          "t2-columns/column_name": "Comment Count",
+          "t2-columns/description": "Public comment count at collection time.",
+          "t2-columns/task_role": "Metadata",
+          "t2-columns/example_value": "19"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "segmentation-prompts",
+      "name": "SegmentationPromptRecords",
+      "description": "Per-class prompt vocabularies used to produce Grounding DINO + SAM2 pseudo-labels.",
+      "key": {
+        "@id": "segmentation-prompts/class_id"
+      },
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "segmentation-prompts/class_id",
+          "name": "class_id",
+          "description": "HUSIC class identifier.",
+          "dataType": "sc:Integer"
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "segmentation-prompts/label",
+          "name": "label",
+          "description": "HUSIC label.",
+          "dataType": "sc:Text"
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "segmentation-prompts/prompt",
+          "name": "prompt",
+          "description": "Per-class Grounding DINO prompt vocabulary.",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "segmentation-prompts/class_id": 0,
+          "segmentation-prompts/label": "Exterior urban spaces with people",
+          "segmentation-prompts/prompt": "person . crowd . pedestrian . building facade . lawn . street lamp . glass curtain wall . sky . tree . shrub . fence . road . water . river . vehicle . sculpture . installation . pavement . street signage . fountain"
+        },
+        {
+          "segmentation-prompts/class_id": 1,
+          "segmentation-prompts/label": "Exterior urban spaces without people",
+          "segmentation-prompts/prompt": "building facade . glass curtain wall . wooden facade . tree . shrub . lawn . sky . pavement . road . water . river . lantern . sculpture . installation . street lamp . signage . fence . bridge . water feature . fountain"
+        },
+        {
+          "segmentation-prompts/class_id": 2,
+          "segmentation-prompts/label": "Interior urban spaces with people",
+          "segmentation-prompts/prompt": "person . shopper . crowd . retail shelf . escalator . elevator . ceiling . floor tile . glass partition . display case . door . indoor plant . wall . window . handrail . column"
+        },
+        {
+          "segmentation-prompts/class_id": 3,
+          "segmentation-prompts/label": "Interior urban spaces without people",
+          "segmentation-prompts/prompt": "retail shelf . escalator . indoor corridor . ceiling . floor tile . marble floor . glass partition . display case . wall . column . indoor plant . elevator . door . window . lighting fixture . handrail"
+        },
+        {
+          "segmentation-prompts/class_id": 4,
+          "segmentation-prompts/label": "Hotel or commercial lodging spaces",
+          "segmentation-prompts/prompt": "hotel bed . furniture . sofa . carpet . marble floor . tile floor . wooden floor . ceiling . bathroom . window . curtain . lamp"
+        },
+        {
+          "segmentation-prompts/class_id": 5,
+          "segmentation-prompts/label": "Private home interiors",
+          "segmentation-prompts/prompt": "sofa . bed . dining table . floor . ceiling . kitchen . bookshelf . wardrobe . window . lamp . carpet . wall"
+        },
+        {
+          "segmentation-prompts/class_id": 6,
+          "segmentation-prompts/label": "Food or drink items",
+          "segmentation-prompts/prompt": "food dish . meal plate . dessert . beverage cup . coffee . drink bottle . bowl . chopsticks . spoon . dining table . person . restaurant interior"
+        },
+        {
+          "segmentation-prompts/class_id": 7,
+          "segmentation-prompts/label": "Retail products and merchandise",
+          "segmentation-prompts/prompt": "fashion clothing . shoes . cosmetics . product package . merchandise . retail shelf . bag . jewelry . electronics . store window . mannequin . person . floor . wall"
+        },
+        {
+          "segmentation-prompts/class_id": 8,
+          "segmentation-prompts/label": "Human-centered portrait",
+          "segmentation-prompts/prompt": "person . face . group photo . building facade . sky . tree . floor . food . animal . vehicle . indoor background"
+        },
+        {
+          "segmentation-prompts/class_id": 9,
+          "segmentation-prompts/label": "Other non-spatial content",
+          "segmentation-prompts/prompt": "animal . person . vehicle . advertisement poster . text . QR code . screenshot . sculpture . meme . sky . plant . signage . graphic design . logo . map . infographic . chat record"
+        }
+      ]
+    }
+  ],
+  "rai:dataLimitations": [
+    "Urban-ImageNet is geographically concentrated in China and should not be interpreted as a universal global urban dataset.",
+    "The source is public Weibo posts, so the corpus reflects platform demographics, posting norms, commercial popularity, hashtag practices, and visibility biases rather than a representative sample of all city users.",
+    "The 1K, 10K, and 100K subsets are intentionally class-balanced for benchmarking; the full 2M corpus is unbalanced and has no predefined train/validation/test split.",
+    "Post-level image-text pairing is naturally loose: one post may contain up to nine images, and the post text may describe an event, emotion, route, or commercial activity rather than a literal caption of every image.",
+    "Task 3 instance masks are model-generated pseudo-labels from Grounding DINO and SAM2 with quality filtering, not exhaustive human pixel annotations.",
+    "The dataset is not recommended for individual identification, face recognition, account reconstruction, surveillance, social scoring, commercial profiling, law-enforcement targeting, or demographic inference about specific users."
+  ],
+  "rai:dataBiases": [
+    "Selection bias arises because only public social-media posts with location-specific commercial-site hashtags were collected.",
+    "Population bias is likely because Weibo users over-represent digitally active, urban, and younger groups compared with all city users.",
+    "Geographic and cultural bias is present because all 24 cities are in China and the text is primarily original Chinese social-media language.",
+    "Visual bias is present because users tend to post photogenic, socially meaningful, popular, or commercial scenes rather than mundane or private experiences.",
+    "The full 2M corpus is class-imbalanced; balanced subsets are useful for model comparison but do not reflect the natural class distribution of the source platform.",
+    "Segmentation pseudo-labels may inherit Grounding DINO and SAM2 biases, including missed small objects, errors on commercial interiors, and prompt vocabulary effects."
+  ],
+  "rai:personalSensitiveInformation": [
+    "The source posts were public, but social-media images and text can still contain contextual personal information. The release therefore uses privacy-protected derivatives rather than raw data.",
+    "Original usernames and account names are removed. Post ID and User ID are released only as opaque numerical identifiers after anonymization/pseudonymization and are not intended to reconstruct original accounts.",
+    "Images are resized to a maximum long edge of 512 px. Faces, license plates, QR-code-like regions, and other sensitive visual regions are blurred with automated detectors followed by human spot checks.",
+    "The raw high-resolution corpus, larger than 4 TB, is not publicly released and is retained securely. The public release is substantially reduced in resolution and metadata detail.",
+    "The dataset does not intentionally provide health, financial, political, religious, biometric, or exact personal identity attributes. Users must not attempt re-identification or linkage to original social-media accounts."
+  ],
+  "rai:dataUseCases": [
+    "Validated use cases include urban scene semantic classification under the 10-class HUSIC taxonomy, category-level image-text retrieval, post-level Chinese image-text retrieval with one-to-many matching, instance segmentation benchmarking, and data-scale behavior analysis across 1K/10K/100K/2M variants.",
+    "The dataset is intended for non-commercial academic research in urban perception, computational urban studies, multimodal learning, image classification, cross-modal retrieval, segmentation, and reproducible benchmarking.",
+    "Construct validity: the dataset is designed to represent user-generated visual and textual traces of urban commercial space perception, not the full demographic distribution of urban residents.",
+    "Uses not validated include production deployment, safety-critical navigation, individual-level behavior prediction, demographic profiling, policing, surveillance, or inference about private individuals."
+  ],
+  "rai:dataSocialImpact": "Potential positive impacts include supporting evidence-based urban planning, helping researchers understand how people perceive and document commercial spaces, improving non-Western and Chinese-language multimodal benchmarks, and enabling public-good research for more inclusive urban design. Potential negative impacts include privacy leakage, re-identification attempts, surveillance-oriented use, account reconstruction, and commercial profiling. Mitigations include using only public posts, anonymizing identifiers, removing usernames, blurring faces and license plates, resizing images, minimizing metadata, restricting use to non-commercial academic research, and explicitly prohibiting re-identification, surveillance, face recognition, and profiling.",
+  "rai:hasSyntheticData": false,
+  "rai:dataCollection": "Urban-ImageNet was collected from public Sina Weibo posts using location-specific hashtags for 61 commercial sites across 24 Chinese cities from 2019-01-01 to 2025-12-31. For each public post, the pipeline retained image attachments, original Chinese post text, timestamp, city/site metadata, place tag, and lightweight engagement metadata.",
+  "rai:dataCollectionType": [
+    "Web Scraping",
+    "User-generated content data",
+    "Software Collection",
+    "Manual Human Curation"
+  ],
+  "rai:dataCollectionRawData": "The raw corpus consisted of public Weibo image-text posts from commercial districts. The raw high-resolution corpus exceeds 4 TB and is not publicly released; only privacy-protected, resized, anonymized derivatives are distributed.",
+  "rai:dataPreprocessingProtocol": [
+    "Perceptual-hash near-duplicate removal, minimum-resolution filtering, automated NSFW filtering, repeated-advertisement/spam removal, metadata minimization, image resizing to max 512 px, face/license-plate/QR-sensitive-region blurring, and human spot-check review.",
+    "Original usernames and direct account identifiers were removed; released Post ID and User ID fields are opaque numerical identifiers."
+  ],
+  "rai:dataAnnotationProtocol": "The HUSIC 10-class labels were manually annotated by trained researchers using standardized guidelines and calibration. Task 3 pseudo-labels were generated with class-specific Grounding DINO prompts followed by SAM2 mask prediction, non-maximum suppression, area filtering, and quality review.",
+  "rai:dataAnnotationPlatform": [
+    "Internal researcher annotation workflow",
+    "Grounding DINO",
+    "SAM2"
+  ],
+  "rai:dataAnnotationAnalysis": "The 100K benchmark set was manually labeled by three trained researchers after calibration; paper documentation reports high inter-rater agreement. Segmentation labels are marked as model-generated pseudo-labels and should be treated differently from exhaustive human pixel annotations.",
+  "rai:annotationsPerItem": "Each benchmark image has one HUSIC class label. Text-image rows provide post-level metadata. Instance segmentation files contain zero or more object annotations per image, depending on model detections and filtering.",
+  "rai:machineAnnotationTools": [
+    "Grounding DINO",
+    "SAM2",
+    "pHash deduplication",
+    "face/license-plate blurring detectors"
+  ],
+  "rai:dataReleaseMaintenancePlan": "The dataset is released as version 1.0 for NeurIPS 2026 submission. Future updates should document checksum changes, privacy-processing changes, annotation revisions, and deprecation of superseded files.",
+  "prov:wasDerivedFrom": [
+    {
+      "@id": "https://weibo.com"
+    },
+    {
+      "@id": "https://huggingface.co/datasets/Yiwei-Ou/Urban-ImageNet"
+    }
+  ],
+  "prov:wasGeneratedBy": [
+    {
+      "@type": "prov:Activity",
+      "@id": "public-weibo-collection",
+      "name": "Public Weibo Commercial-Site Collection",
+      "prov:type": "DataCollection",
+      "prov:startedAtTime": "2019-01-01",
+      "prov:endedAtTime": "2025-12-31",
+      "description": "Collected public posts associated with location-specific hashtags for 61 commercial sites in 24 Chinese cities."
+    },
+    {
+      "@type": "prov:Activity",
+      "@id": "privacy-preserving-preprocessing",
+      "name": "Privacy-Preserving Preprocessing",
+      "prov:type": "DataProcessing",
+      "description": "Applied deduplication, filtering, metadata minimization, anonymization, resizing, face/license-plate/QR-sensitive-region blurring, and manual spot checks."
+    },
+    {
+      "@type": "prov:Activity",
+      "@id": "husic-manual-annotation",
+      "name": "HUSIC Manual Annotation",
+      "prov:type": "DataAnnotation",
+      "description": "Trained researchers annotated the balanced benchmark subsets using the 10-class HUSIC taxonomy and calibration guidelines."
+    },
+    {
+      "@type": "prov:Activity",
+      "@id": "groundingdino-sam2-pseudolabeling",
+      "name": "Grounding DINO + SAM2 Instance Segmentation Pseudo-Labeling",
+      "prov:type": "DataAnnotation",
+      "description": "Generated boxes, detected labels, confidence scores, and COCO RLE masks using class-specific prompt vocabularies, NMS, area filtering, and review."
+    }
+  ]
+}