# Towards Large-scale Building Attribute Mapping using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved

arXiv: https://arxiv.org/abs/2309.08042 · HTML: https://mlsanity.com/paper/2309.08042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images](https://mlsanity.com/paper/1601.07140/) (2016)
- [Show, Attend and Read: A Simple and Strong Baseline for Irregular Text Recognition](https://mlsanity.com/paper/1811.00751/) (2018)
- [TextSnake: A Flexible Representation for Detecting Text of Arbitrary Shapes](https://mlsanity.com/paper/1807.01544/) (2018)
- [Building Instance Classification Using Street View Images](https://mlsanity.com/paper/1802.09026/) (2018)
- [Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks](https://mlsanity.com/paper/2111.09460/) (2021)
- [CG-Net: Conditional GIS-aware Network for Individual Building Segmentation in VHR SAR Images](https://mlsanity.com/paper/2011.08362/) (2020)
- [Instance segmentation of buildings using keypoints](https://mlsanity.com/paper/2006.03858/) (2020)
- [Using Social Media Images for Building Function Classification](https://mlsanity.com/paper/2202.07315/) (2022)

## Built on by (most cited first, 1 of 1)

- [Building Floor Number Estimation from Crowdsourced Street-Level Images: Munich Dataset and Baseline Method](https://mlsanity.com/paper/2505.18021/) (2025)

## Abstract (arXiv)

Crowdsourced platforms provide huge amounts of street-view images that contain valuable building information. This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping. We use Flickr images, particularly examining texts on building facades. A Berlin Flickr dataset is created, and pre-trained STR models are used for text detection and recognition. Manual checking on a subset of STR-recognized images demonstrates high accuracy. We examined the correlation between STR results and building functions, and analysed instances where texts were recognized on residential buildings but not on commercial ones. Further investigation revealed significant challenges associated with this task, including small text regions in street-view images, the absence of ground truth labels, and mismatches in buildings in Flickr images and building footprints in OpenStreetMap (OSM). To develop city-wide mapping beyond urban hotspot locations, we suggest differentiating the scenarios where STR proves effective while developing appropriate algorithms or bringing in additional data for handling other cases. Furthermore, interdisciplinary collaboration should be undertaken to understand the motivation behind building photography and labeling. The STR-on-Flickr results are publicly available at https://github.com/ya0-sun/STR-Berlin.