# Map Generation from Large Scale Incomplete and Inaccurate Data Labels

arXiv: https://arxiv.org/abs/2005.10053 · HTML: https://mlsanity.com/paper/2005.10053/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2020.

## Built on by (most cited first, 5 of 5)

- [AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning](https://mlsanity.com/paper/2202.00067/) (2022)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [Aboveground carbon biomass estimate with Physics-informed deep network](https://mlsanity.com/paper/2210.13752/) (2022)
- [Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2402.16164/) (2024)
- [Runway Extraction and Improved Mapping from Space Imagery](https://mlsanity.com/paper/2201.00848/) (2021)

## Abstract (arXiv)

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.