# AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

arXiv: https://arxiv.org/abs/2202.00067 · HTML: https://mlsanity.com/paper/2202.00067/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 56 references that are in the map.
- First posted on arXiv 31 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Exploring the Limits of Weakly Supervised Pretraining](https://mlsanity.com/paper/1805.00932/) (2018)
- [BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understanding](https://mlsanity.com/paper/1902.06148/) (2019)
- [Billion-scale semi-supervised learning for image classification](https://mlsanity.com/paper/1905.00546/) (2019)
- [Deep learning in remote sensing: a review](https://mlsanity.com/paper/1710.03959/) (2017)
- [Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities](https://mlsanity.com/paper/2005.01094/) (2020)
- [SpaceNet 6: Multi-Sensor All Weather Mapping Dataset](https://mlsanity.com/paper/2004.06500/) (2020)
- [DeepSat - A Learning framework for Satellite Imagery](https://mlsanity.com/paper/1509.03602/) (2015)
- [Map Generation from Large Scale Incomplete and Inaccurate Data Labels](https://mlsanity.com/paper/2005.10053/) (2020)
- [Monitoring the Impact of Wildfires on Tree Species with Deep Learning](https://mlsanity.com/paper/2011.02514/) (2020)
- [PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Data](https://mlsanity.com/paper/2012.06907/) (2020)
- [Learning and Recognizing Archeological Features from LiDAR Data](https://mlsanity.com/paper/2004.02099/) (2020)
- [Quantification of Carbon Sequestration in Urban Forests](https://mlsanity.com/paper/2106.00182/) (2021)

## Built on by (most cited first, 6 of 6)

- [AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentation](https://mlsanity.com/paper/2403.01641/) (2024)
- [Monitoring Urban Forests from Auto-Generated Segmentation Maps](https://mlsanity.com/paper/2206.06948/) (2022)
- [Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentation](https://mlsanity.com/paper/2402.16164/) (2024)
- [DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resilience](https://mlsanity.com/paper/2306.06269/) (2023)
- [Impact of Label Types on Training SWIN Models with Overhead Imagery](https://mlsanity.com/paper/2310.07572/) (2023)
- [AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing](https://mlsanity.com/paper/2405.13993/) (2024)

## Abstract (arXiv)

A key challenge of supervised learning is the availability of human-labeled data. We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. It is based on rasterized statistical features extracted from surveys such as e.g. LiDAR measurements. Using simple combinations of the rasterized statistical layers, it is demonstrated that multiple classes can be generated at accuracies of ~0.9. As proof of concept, we utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas with multiple land cover classes. The general method proposed here is platform independent, and it can be adapted to generate labels for other satellite modalities in order to enable machine learning on overhead imagery for land use classification and object detection.