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README.md

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 pretty_name: GenAI Sustainable Streetscape Dataset
 size_categories:
 - 10K<n<100K
----
+---
+
+# GenAI Sustainable Streetscape Dataset
+
+**Official dataset accompanying the manuscript**
+
+> **Reframing Urban "Sustainable" Streetscapes: Evidence from Cross-City Transformation and Convergence**
+
+!UNDER REVIEW!
+
+## Overview
+
+This dataset contains paired **Google Street View images**, **AI-generated sustainable streetscape transformations**, and **semantic segmentation outputs** used to investigate how a zero-shot foundation model (OpenAI GPT-5 image-to-image) transforms urban streetscapes across different cities. The associated study evaluates whether AI-generated "sustainable" streets remain context-sensitive or converge toward a common visual prototype.
+
+The dataset includes three cities representing distinct urban contexts:
+
+| City | Images |
+|-------|-------:|
+| Jakarta | 567 |
+| Melbourne | 832 |
+| Singapore | 694 |
+| **Total** | **2,093** |
+
+For every street-view image, the repository provides:
+
+- Original Street View Image
+- AI-generated baseline transformation
+- Semantic segmentation outputs
+- Pixel statistics
+- Overlay visualization
+- Panel visualization
+- Alternative prompt generations
+- Manual IoU annotations for segmentation validation
+
+---
+
+# Dataset Structure
+
+```
+genai_sustainablestreetscape/
+│
+├── jakarta/
+├── melbourne/
+├── singapore/
+│
+├── alternative-prompts/
+│
+└── iou/
+```
+
+---
+
+# City folders
+
+Each city folder contains the same directory structure.
+
+```
+jakarta/
+│
+├── image/
+│      jakarta_2.png
+│      ...
+│
+├── baseline/
+│      jakarta_2_noncontext.png
+│      ...
+│
+├── baseline-resize/
+│      jakarta_2_noncontext.png
+│      ...
+│
+├── point/
+│      jakarta_points_streetview.csv
+│
+├── segmented-image/
+│      overlay/
+│      segmented/
+│      panels/
+│      pixel_counts.csv
+│      pixel_counts.xlsx
+│
+├── segmented-baseline/
+│      overlay/
+│      segmented/
+│      panels/
+│      pixel_counts.csv
+│      pixel_counts.xlsx
+│
+└── segmented-baseline-resize/
+       overlay/
+       segmented/
+       panels/
+       pixel_counts.csv
+       pixel_counts.xlsx
+```
+
+The same directory structure is used for:
+
+- `melbourne/`
+- `singapore/`
+
+---
+
+# Alternative Prompt Experiments
+
+The `alternative-prompts/` directory contains GPT-5 image-to-image generations produced using four prompting strategies. These experiments were designed to evaluate how different prompt formulations influence the visual interpretation of **urban sustainability**, and whether GPT-5 consistently converges toward similar streetscape transformations despite changes in prompt wording.
+
+```
+alternative-prompts/
+├── baseline_resize/                (n = 208)
+├── explicit_resize/                (n = 120)
+├── non_vegetation_resize/          (n = 120)
+└── sustainable_movement_resize/    (n = 120)
+```
+
+Each subfolder contains AI-generated streetscape images corresponding to one prompting strategy. The image naming convention follows the format:
+
+```
+jakarta_26_noncontext.png
+jakarta_26_explicit.png
+jakarta_26_non_vegetation.png
+jakarta_26_sustainable_movement.png
+```
+
+## Prompting Strategies
+
+### 1. Baseline *(Main Experiment)*
+
+The baseline prompt was used to generate all images analyzed throughout the main manuscript and served as the reference condition.
+
+> *Generate a realistic transformation of the attached street-view image (1024 × 1024) with the goal of making the streetscape appear more sustainable. Maintain the original structure and urban layout, but creatively reinterpret the scene through a sustainability-focused lens. Emphasize visual realism and plausible urban design improvements. The image is a street-view image taken in **{city}**, capturing its typical urban character.*
+
+---
+
+### 2. Explicit Multidimensional Sustainability
+
+This prompt explicitly defines sustainability as a multidimensional concept, encouraging the model to consider multiple urban design interventions rather than inferring sustainability implicitly.
+
+> *Generate a realistic transformation of the attached street-view image (1024 × 1024) with the goal of making the streetscape appear more sustainable. Sustainability improvements may include, but are not limited to, pedestrian walkability, cycling infrastructure, public transit visibility, vegetation and greenery, and public social spaces. Maintain the original structure and urban layout while creatively reinterpreting the scene through a sustainability-focused lens. Emphasize visual realism and plausible urban design improvements. The image is a street-view image taken in **{city}**, capturing its typical urban character.*
+
+---
+
+### 3. Non-Vegetation Constraint
+
+This prompt examines whether GPT-5 can generate sustainable streetscapes without relying on additional greenery, instead emphasizing transportation-related interventions.
+
+> *Generate a realistic transformation of the attached street-view image (1024 × 1024) with the goal of making the streetscape appear more sustainable, focusing specifically on pedestrian infrastructure, cycling infrastructure, and public transit elements. **Do not add or expand vegetation, greenery, or natural landscaping.** Maintain the original structure and urban layout while improving walkability, bikeability, and shared mobility infrastructure. Emphasize visual realism and plausible urban design improvements. The image is a street-view image taken in **{city}**, capturing its typical urban character.*
+
+---
+
+### 4. Sustainable Movement
+
+This prompt prioritizes active mobility by emphasizing infrastructure supporting walking and cycling.
+
+> *Generate a realistic transformation of the attached street-view image (1024 × 1024) with the goal of making the streetscape more supportive of sustainable movement, combining pedestrian-friendly and cycling-friendly design. Emphasize wider and safer sidewalks, protected bicycle lanes, bicycle parking, and clearer pedestrian crossings while maintaining the original structure and urban layout. Emphasize visual realism and plausible urban design improvements. The image is a street-view image taken in **{city}**, capturing its typical urban character.*
+
+## Purpose
+
+The alternative prompt experiments were conducted as a robustness analysis to evaluate the sensitivity of GPT-5's streetscape transformations to prompt wording. By systematically varying the conceptual emphasis—from a generic sustainability prompt to explicit multidimensional, vegetation-constrained, and mobility-focused prompts—the experiments investigate whether AI-generated sustainable streetscapes converge toward a common visual representation or remain responsive to different interpretations of sustainability.
+
+---
+
+# IoU Validation
+
+The `iou/` folder contains manually annotated LabelMe polygons used to validate semantic segmentation.
+
+Example:
+
+```
+jakarta_26_noncontext_labelme.json
+melbourne_1_noncontext_labelme.json
+singapore_10_noncontext_labelme.json
+```
+
+Approximately **10%** of generated images from each city were manually annotated to compute Intersection over Union (IoU).
+
+---
+
+# Semantic Categories
+
+Mask2Former (Mapillary Vistas) predictions are regrouped into eight semantic categories:
+
+- Sky
+- Vegetation
+- Built Structure
+- Road Infrastructure
+- Pedestrian Infrastructure
+- Bikeability Infrastructure
+- Vehicle
+- Street Furniture
+
+Pixel proportions for each image are provided in:
+
+```
+pixel_counts.csv
+pixel_counts.xlsx
+```
+
+---
+
+# Image Naming Convention
+
+Original image
+
+```
+jakarta_2.png
+```
+
+Baseline GPT-5 generation
+
+```
+jakarta_2_noncontext.png
+```
+
+Semantic segmentation
+
+```
+jakarta_2_noncontext_seg.png
+```
+
+Visualization overlay
+
+```
+jakarta_2_noncontext_overlay.png
+```
+
+Visualization panel
+
+```
+jakarta_2_noncontext_panel.png
+```
+
+---
+
+# Methodology
+
+The complete workflow consists of five stages:
+
+1. Street View image collection
+2. GPT-5 image-to-image transformation
+3. Semantic segmentation using Mask2Former
+4. UMAP embedding and K-Means clustering
+5. Homogenization and cross-city analysis
+
+Further methodological details are available in the accompanying manuscript. :contentReference[oaicite:4]{index=4}
+
+---
+
+# Applications
+
+This dataset can be used for:
+
+- Street-view semantic segmentation
+- Urban visual perception research
+- Sustainable streetscape analysis
+- Generative AI evaluation
+- Prompt engineering
+- Image-to-image translation benchmarking
+- Cross-city visual comparison
+- Urban morphology studies
+
+---
+
+# Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@article{pradana2026genai,
+  title={Reframing Urban "Sustainable" Streetscapes: Evidence from Cross-City Transformation and Convergence},
+  author={Pradana, Mohammad Raditia and Gamal, Ahmad and Aryal, Jagannath},
+  year={2026},
+  note={Manuscript under review}
+}
+```
+
+---
+
+# License
+
+This dataset is released under the **CC BY 4.0** license.
+
+Please ensure that the usage of Google Street View imagery complies with Google's Terms of Service.
+
+---
+
+# Contact
+
+**Mohammad Raditia Pradana**
+
+SMART CITY  
+Department of Geography  
+Universitas Indonesia
+
+Email: mohammad.raditia03@ui.ac.id  
+Website: https://aditpradana36.github.io/  
+GitHub: https://github.com/AditPradana36
+
+---
+
+# Acknowledgements
+
+This work was supported by the Indonesian Endowment Fund for Education (LPDP), Universitas Indonesia, and The University of Melbourne.