# Correcting rural building annotations in OpenStreetMap using convolutional neural networks

arXiv: https://arxiv.org/abs/1901.08190 · HTML: https://mlsanity.com/paper/1901.08190/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [CRASAR-U-DROIDs: A Large Scale Benchmark Dataset for Building Alignment and Damage Assessment in Georectified sUAS Imagery](https://mlsanity.com/paper/2407.17673/) (2024)
- [Fine-grained Population Mapping from Coarse Census Counts and Open Geodata](https://mlsanity.com/paper/2211.04039/) (2022)
- [Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters](https://mlsanity.com/paper/2405.06593/) (2024)
- [Urban land-use analysis using proximate sensing imagery: a survey](https://mlsanity.com/paper/2101.04827/) (2021)
- [ML Updates for OpenStreetMap: Analysis of Research Gaps and Future Directions](https://mlsanity.com/paper/2407.03365/) (2024)
- [Deploying Rapid Damage Assessments from sUAS Imagery for Disaster Response](https://mlsanity.com/paper/2511.03132/) (2025)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [CoFi: Coarse-to-Fine ICP for LiDAR Localization in an Efficient Long-lasting Point Cloud Map](https://mlsanity.com/paper/2110.10194/) (2021)
- [Extracting the U.S. building types from OpenStreetMap data](https://mlsanity.com/paper/2409.05692/) (2024)
- [Fusion of Pervasive RF Data with Spatial Images via Vision Transformers for Enhanced Mapping in Smart Cities](https://mlsanity.com/paper/2508.03736/) (2025)
- [A Benchmark Dataset for Spatially Aligned Road Damage Assessment in Small Uncrewed Aerial Systems Disaster Imagery](https://mlsanity.com/paper/2512.12128/) (2025)

## Abstract (arXiv)

Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems with these annotations essentially fall into three categories: (i) most commonly, many annotations are geometrically misaligned with the updated imagery; (ii) some annotations do not correspond to buildings in the images (they are misannotations or the buildings have been destroyed); and (iii) some annotations are missing for buildings in the images (the buildings were never annotated or were built between subsequent image acquisitions). First, we propose a method based on Markov Random Field (MRF) to align the buildings with their annotations. The method maximizes the correlation between annotations and a building probability map while enforcing that nearby buildings have similar alignment vectors. Second, the annotations with no evidence in the building probability map are removed. Third, we present a method to detect non-annotated buildings with predefined shapes and add their annotation. The proposed methodology shows considerable improvement in accuracy of the OSM annotations for two regions of Tanzania and Zimbabwe, being more accurate than state-of-the-art baselines.