# A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition

arXiv: https://arxiv.org/abs/2112.05794 · HTML: https://mlsanity.com/paper/2112.05794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Normalized Cut Loss for Weakly-supervised CNN Segmentation](https://mlsanity.com/paper/1804.01346/) (2018)
- [An Entropic Optimal Transport Loss for Learning Deep Neural Networks under Label Noise in Remote Sensing Images](https://mlsanity.com/paper/1810.01163/) (2018)
- [Learning to segment from misaligned and partial labels](https://mlsanity.com/paper/2005.13180/) (2020)

## Built on by (most cited first, 1 of 1)

- [DIGMAPPER: A Modular System for Automated Geologic Map Digitization](https://mlsanity.com/paper/2506.16006/) (2025)

## Abstract (arXiv)

Thousands of scanned historical topographic maps contain valuable information covering long periods of time, such as how the hydrography of a region has changed over time. Efficiently unlocking the information in these maps requires training a geospatial objects recognition system, which needs a large amount of annotated data. Overlapping geo-referenced external vector data with topographic maps according to their coordinates can annotate the desired objects' locations in the maps automatically. However, directly overlapping the two datasets causes misaligned and false annotations because the publication years and coordinate projection systems of topographic maps are different from the external vector data. We propose a label correction algorithm, which leverages the color information of maps and the prior shape information of the external vector data to reduce misaligned and false annotations. The experiments show that the precision of annotations from the proposed algorithm is 10% higher than the annotations from a state-of-the-art algorithm. Consequently, recognition results using the proposed algorithm's annotations achieve 9% higher correctness than using the annotations from the state-of-the-art algorithm.