# ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science arXiv: https://arxiv.org/abs/2412.18641 ยท HTML: https://mlsanity.com/paper/2412.18641/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 158 references that are in the map. - First posted on arXiv 24 Dec 2024. ## Builds on (most cited first, 30 of 33) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022) - [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023) - [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021) - [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021) - [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review](https://mlsanity.com/paper/2005.09830/) (2020) - [OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks](https://mlsanity.com/paper/1611.01890/) (2016) - [Learning to Navigate in Cities Without a Map](https://mlsanity.com/paper/1804.00168/) (2018) - [Deep Cosine Metric Learning for Person Re-Identification](https://mlsanity.com/paper/1812.00442/) (2018) - [Deep Learning the City : Quantifying Urban Perception At A Global Scale](https://mlsanity.com/paper/1608.01769/) (2016) - [Building Instance Classification Using Street View Images](https://mlsanity.com/paper/1802.09026/) (2018) - [Towards Universal Image Embeddings: A Large-Scale Dataset and Challenge for Generic Image Representations](https://mlsanity.com/paper/2309.01858/) (2023) - [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017) - [Take a Look Around: Using Street View and Satellite Images to Estimate House Prices](https://mlsanity.com/paper/1807.07155/) (2018) - [Assessing bikeability with street view imagery and computer vision](https://mlsanity.com/paper/2105.08499/) (2021) - [To use or not to use proprietary street view images in (health and place) research? That is the question](https://mlsanity.com/paper/2402.11504/) (2024) - [A citizen science toolkit to collect human perceptions of urban environments using open street view images](https://mlsanity.com/paper/2403.00174/) (2024) - [Medical Image Retrieval Using Pretrained Embeddings](https://mlsanity.com/paper/2311.13547/) (2023) - [Effectiveness of Deep Image Embedding Clustering Methods on Tabular Data](https://mlsanity.com/paper/2212.14111/) (2022) - [Streetify: Using Street View Imagery And Deep Learning For Urban Streets Development](https://mlsanity.com/paper/1911.08007/) (2019) - [The Propensity to Cycle Tool: An open source online system for sustainable transport planning](https://mlsanity.com/paper/1509.04425/) (2015) ## Built on by (most cited first, 3 of 3) - [OpenFACADES: An Open Framework for Architectural Caption and Attribute Data Enrichment via Street View Imagery](https://mlsanity.com/paper/2504.02866/) (2025) - [It is not always greener on the other side: Greenery perception across demographics and personalities in multiple cities](https://mlsanity.com/paper/2512.17186/) (2025) - [City landscape in sight: A crowdsourced framework for unlocking urban-scale window view perceptions from real estate imagery](https://mlsanity.com/paper/2606.15198/) (2026) ## Abstract (arXiv) Street view imagery (SVI) has been instrumental in many studies in the past decade to understand and characterize street features and the built environment. Researchers across a variety of domains, such as transportation, health, architecture, human perception, and infrastructure have employed different methods to analyze SVI. However, these applications and image-processing procedures have not been standardized, and solutions have been implemented in isolation, often making it difficult for others to reproduce existing work and carry out new research. Using SVI for research requires multiple technical steps: accessing APIs for scalable data collection, preprocessing images to standardize formats, implementing computer vision models for feature extraction, and conducting spatial analysis. These technical requirements create barriers for researchers in urban studies, particularly those without extensive programming experience. We developed ZenSVI, a free and open-source Python package that integrates and implements the entire process of SVI analysis, supporting a wide range of use cases. Its end-to-end pipeline includes downloading SVI from multiple platforms (e.g., Mapillary and KartaView) efficiently, analyzing metadata of SVI, applying computer vision models to extract target features, transforming SVI into different projections (e.g., fish-eye and perspective) and different formats (e.g., depth map and point cloud), visualizing analyses with maps and plots, and exporting outputs to other software tools. We demonstrated its use in Singapore through a case study of data quality assessment and clustering analysis in a streamlined manner. Our software improves the transparency, reproducibility, and scalability of research relying on SVI and supports researchers in conducting urban analyses efficiently. Its modular design facilitates extensions of the package for new use cases.