mlsanityData to 7 Oct 2026

AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning

arXiv 2202.00067 ↗First posted 31 Jan 2022eess.IV cs.CV cs.LGcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 14 of its 56 references that are in the map. First posted on arXiv 31 Jan 2022.

Builds on

14 of 56 references.

References · 14

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2019Self-supervised Visual Feature Learning with Deep Neural Networks: A Surveycited by 656
  3. 2018Exploring the Limits of Weakly Supervised Pretrainingcited by 604
  4. 2019Billion-scale semi-supervised learning for image classificationcited by 232
  5. 2019BigEarthNet: A Large-Scale Benchmark Archive For Remote Sensing Image Understandingcited by 231
  6. 2017Deep learning in remote sensing: a reviewcited by 128
  7. 2020Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunitiescited by 71
  8. 2020SpaceNet 6: Multi-Sensor All Weather Mapping Datasetcited by 47
  9. 2015DeepSat - A Learning framework for Satellite Imagerycited by 37
  10. 2020Map Generation from Large Scale Incomplete and Inaccurate Data Labelscited by 5
  11. 2020Monitoring the Impact of Wildfires on Tree Species with Deep Learningcited by 4
  12. 2020PAIRS AutoGeo: an Automated Machine Learning Framework for Massive Geospatial Datacited by 3
2 more
  1. 2020Learning and Recognizing Archeological Features from LiDAR Datacited by 2
  2. 2021Quantification of Carbon Sequestration in Urban Forestscited by 2

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 3.

6 papers.

  1. 2024AIO2: Online Correction of Object Labels for Deep Learning with Incomplete Annotation in Remote Sensing Image Segmentationcited by 9
  2. 2022Monitoring Urban Forests from Auto-Generated Segmentation Mapscited by 4
  3. 2024Task Specific Pretraining with Noisy Labels for Remote Sensing Image Segmentationcited by 4
  4. 2023DeepLCZChange: A Remote Sensing Deep Learning Model Architecture for Urban Climate Resiliencecited by 2
  5. 2023Impact of Label Types on Training SWIN Models with Overhead Imagerycited by 0
  6. 2024AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensingcited by 0