# EarthMapper: A Tool Box for the Semantic Segmentation of Remote Sensing Imagery

arXiv: https://arxiv.org/abs/1804.00292 · HTML: https://mlsanity.com/paper/1804.00292/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [Hyperspectral image segmentation with a machine learning model trained using quantum annealer](https://mlsanity.com/paper/2503.01400/) (2025)
- [Post Processing of image segmentation using Conditional Random Fields](https://mlsanity.com/paper/2510.09833/) (2025)

## Abstract (arXiv)

Deep learning continues to push state-of-the-art performance for the semantic segmentation of color (i.e., RGB) imagery; however, the lack of annotated data for many remote sensing sensors (i.e. hyperspectral imagery (HSI)) prevents researchers from taking advantage of this recent success. Since generating sensor specific datasets is time intensive and cost prohibitive, remote sensing researchers have embraced deep unsupervised feature extraction. Although these methods have pushed state-of-the-art performance on current HSI benchmarks, many of these tools are not readily accessible to many researchers. In this letter, we introduce a software pipeline, which we call EarthMapper, for the semantic segmentation of non-RGB remote sensing imagery. It includes self-taught spatial-spectral feature extraction, various standard and deep learning classifiers, and undirected graphical models for post-processing. We evaluated EarthMapper on the Indian Pines and Pavia University datasets and have released this code for public use.