Update README.md

README.md

@@ -164,19 +164,46 @@ HUSIC class boundaries are defined by domain-expert concepts rather than data-dr
 |    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 — which varies enormously across research questions — 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 — Exterior *with people* | Human behaviour in public space | Pose estimation, action recognition, pedestrian counting; age/gender distribution; temporal activity mapping |
+| ID 1 — Exterior *without people* | Architectural perception and design quality | Further classify by style (classical / modernist / parametric), space type (plaza / park / streetscape), or façade material |
+| ID 2 — Interior *with people* | Commercial interior vitality | Crowd density, dwell behaviour, wayfinding; how spatial layout influences visitor flow |
+| ID 3 — Interior *without people* | Retail design and aesthetic perception | Classify by interior style, lighting, or spatial configuration |
+| ID 4 — Hotel / lodging | Urban tourism and short-term rental market | How commercial district proximity influences lodging aesthetics and pricing signals |
+| ID 5 — Private home interiors | Urban housing and long-term rental market | How proximity to commercial hubs shapes residential presentation and rental marketing |
+| ID 6 — 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 — Retail products | Consumer behaviour and market analysis | Category trends (fashion / electronics / cosmetics); temporal trend tracking |
+| ID 8 — Human-centered portrait | Social behaviour and place attachment | Which spatial settings motivate photographic self-documentation; social gathering pattern analysis |
+| ID 9 — 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 enable future research on pedestrian behaviour, social activity patterns, human action recognition, and spatial vitality measurement.
-
-- *Without people* classes isolate architectural and design features that attract attention and generate place resonance, supporting aesthetic perception and visual quality studies.
+- *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 — 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 observed in the data: users frequently post hotel and private-home interiors alongside commercial-district imagery, revealing the influence of urban commercial centres on local short-term and long-term rental markets — a research direction not addressed by any existing benchmark.
+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 — a research direction not addressed by any existing benchmark.
 
 ### Rationale: Non-Spatially Relevant Classes (IDs 6–9)
 
-Non-spatial classes capture consumption and social dimensions of urban life. IDs 6–7 correspond to the two primary material goods consumed in commercial centres (food and merchandise). ID 8 captures social gathering and self-expression. ID 9 is a residual class for filtering noise, and together these classes provide concentrated sub-datasets for consumer behaviour and social pattern studies independent of spatial analysis. In the Urban-ImageNet pipeline, these classes also serve as the primary filtering layer to isolate space-relevant imagery for downstream urban perception tasks.
+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 — Food or drink items:** UGC food images carry implicit preference signals — frequency of posting reflects popularity and memorability without requiring explicit ratings. Useful for F&B market research and restaurant analytics.
+- **ID 7 — 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 — Human-centered portrait:** Self-portraits and group photos document the social occasions that spaces enable — 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 — Other non-spatial content:** A residual noise class — advertisements, screenshots, memes, infographics. Minimal relevance for urban or commercial research; its isolation as a discrete class makes downstream filtering reliable and auditable.
 
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@@ -480,7 +507,7 @@ Post-level retrieval difficulty grows naturally as the candidate gallery expands
 
 <!-- Replace with actual pipeline figure -->
 ![Dataset Construction Pipeline](Figures/06-Data-Collection.png)
-*Figure 7: Overview of the Urban-ImageNet dataset construction and annotation pipeline — from Weibo crawling through privacy processing, HUSIC annotation, and multi-task organisation.*
+*Figure 6: Overview of the Urban-ImageNet dataset construction and annotation pipeline — from Weibo crawling through privacy processing, HUSIC annotation, and multi-task organisation.*
 
 Urban-ImageNet was constructed through a five-stage pipeline:
 
@@ -498,7 +525,11 @@ Urban-ImageNet was constructed through a five-stage pipeline:
 
 ## Geographic and Site Coverage
 
-Urban-ImageNet covers **61 urban commercial sites** across **24 Chinese cities** spanning **8 macro-regions**, including leading first-tier cities (Beijing, Shanghai, Chengdu, Guangzhou, Shenzhen), second-tier cities, and regional centres. Sites include both enclosed shopping malls and open-block mixed-use commercial precincts. The full site list and geographic distribution map are provided in the [paper appendix](https://arxiv.org/abs/2605.09936).
+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). 
+
+<!-- Replace with your actual geographic distribution figure -->
+![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.*
 
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