# OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

arXiv: https://arxiv.org/abs/2007.06277 · HTML: https://mlsanity.com/paper/2007.06277/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2020.

## Built on by (most cited first, 30 of 30)

- [Generate Your Own Scotland: Satellite Image Generation Conditioned on Maps](https://mlsanity.com/paper/2308.16648/) (2023)
- [Towards a Collective Agenda on AI for Earth Science Data Analysis](https://mlsanity.com/paper/2104.05107/) (2021)
- [Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images](https://mlsanity.com/paper/2108.12611/) (2021)
- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [GeoVectors: A Linked Open Corpus of OpenStreetMap Embeddings on World Scale](https://mlsanity.com/paper/2108.13092/) (2021)
- [GeoLink: Empowering Remote Sensing Foundation Model with OpenStreetMap Data](https://mlsanity.com/paper/2509.26016/) (2025)
- [Fine-grained Population Mapping from Coarse Census Counts and Open Geodata](https://mlsanity.com/paper/2211.04039/) (2022)
- [A Satellite Imagery Dataset for Long-Term Sustainable Development in United States Cities](https://mlsanity.com/paper/2308.00465/) (2023)
- [From Pixels to Progress: Generating Road Network from Satellite Imagery for Socioeconomic Insights in Impoverished Areas](https://mlsanity.com/paper/2406.11282/) (2024)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [Non-Uniform Spatial Alignment Errors in sUAS Imagery From Wide-Area Disasters](https://mlsanity.com/paper/2405.06593/) (2024)
- [DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery](https://mlsanity.com/paper/2502.10060/) (2025)
- [Urban land-use analysis using proximate sensing imagery: a survey](https://mlsanity.com/paper/2101.04827/) (2021)
- [Attention-Based Vandalism Detection in OpenStreetMap](https://mlsanity.com/paper/2201.10406/) (2022)
- [An unsupervised, open-source workflow for 2D and 3D building mapping from airborne LiDAR data](https://mlsanity.com/paper/2205.14585/) (2022)
- [Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbations](https://mlsanity.com/paper/2307.13131/) (2023)
- [Language and Planning in Robotic Navigation: A Multilingual Evaluation of State-of-the-Art Models](https://mlsanity.com/paper/2501.05478/) (2025)
- [The Actor-Critic Update Order Matters for PPO in Federated Reinforcement Learning](https://mlsanity.com/paper/2506.01261/) (2025)
- [Learning to integrate vision data into road network data](https://mlsanity.com/paper/2112.10624/) (2021)
- [Credible Remote Sensing Scene Classification Using Evidential Fusion on Aerial-Ground Dual-view Images](https://mlsanity.com/paper/2301.00622/) (2023)
- [Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment](https://mlsanity.com/paper/2407.17716/) (2024)
- [A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities](https://mlsanity.com/paper/2604.19653/) (2026)
- [Spatial Representation Learning Beyond Pixels: Unifying Raster Data and Vector Semantics for Human-Centric Geospatial Foundation Models](https://mlsanity.com/paper/2606.02374/) (2026)
- [StructuredMesh: 3D Structured Optimization of Faccade Components on Photogrammetric Mesh Models using Binary Integer Programming](https://mlsanity.com/paper/2306.04184/) (2023)
- [Probabilistic Image-Driven Traffic Modeling via Remote Sensing](https://mlsanity.com/paper/2403.05521/) (2024)
- [Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments](https://mlsanity.com/paper/2409.06730/) (2024)
- [Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Vienna](https://mlsanity.com/paper/2503.15653/) (2025)
- [A Multi-tiered Human-in-the-loop Approach for Interactive School Mapping Using Earth Observation and Machine Learning](https://mlsanity.com/paper/2510.27460/) (2025)
- [Uncertainty-Aware Hierarchical Re-Localization in OpenStreetMap via Semantic Alignment](https://mlsanity.com/paper/2603.01613/) (2026)
- [SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning](https://mlsanity.com/paper/2607.01139/) (2026)

## Abstract (arXiv)

OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in {Geosciences}, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or {land use} classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping.