# Land Cover Change Detection via Semantic Segmentation

arXiv: https://arxiv.org/abs/1911.12903 · HTML: https://mlsanity.com/paper/1911.12903/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping](https://mlsanity.com/paper/2105.02963/) (2021)
- [Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping](https://mlsanity.com/paper/2108.07323/) (2021)
- [Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing](https://mlsanity.com/paper/2301.12541/) (2023)

## Abstract (arXiv)

This paper presents a change detection method that identifies land cover changes from aerial imagery, using semantic segmentation, a machine learning approach. We present a land cover classification training pipeline with Deeplab v3+, state-of-the-art semantic segmentation technology, including data preparation, model training for seven land cover types, and model exporting modules. In the land cover change detection system, the inputs are images retrieved from Google Earth at the same location but from different times. The system then predicts semantic segmentation results on these images using the trained model and calculates the land cover class percentage for each input image. We see an improvement in the accuracy of the land cover semantic segmentation model, with a mean IoU of 0.756 compared to 0.433, as reported in the DeepGlobe land cover classification challenge. The land cover change detection system that leverages the state-of-the-art semantic segmentation technology is proposed and can be used for deforestation analysis, land management, and urban planning.