# Model Generalization in Deep Learning Applications for Land Cover Mapping

arXiv: https://arxiv.org/abs/2008.10351 · HTML: https://mlsanity.com/paper/2008.10351/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Remote Sensing Image Classification with the SEN12MS Dataset](https://mlsanity.com/paper/2104.00704/) (2021)
- [AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10](https://mlsanity.com/paper/2411.15263/) (2024)
- [Research Trends and Applications of Data Augmentation Algorithms](https://mlsanity.com/paper/2207.08817/) (2022)
- [SAda-Net: A Self-Supervised Adaptive Stereo Estimation CNN For Remote Sensing Image Data](https://mlsanity.com/paper/2410.13500/) (2024)

## Abstract (arXiv)

Recent work has shown that deep learning models can be used to classify land-use data from geospatial satellite imagery. We show that when these deep learning models are trained on data from specific continents/seasons, there is a high degree of variability in model performance on out-of-sample continents/seasons. This suggests that just because a model accurately predicts land-use classes in one continent or season does not mean that the model will accurately predict land-use classes in a different continent or season. We then use clustering techniques on satellite imagery from different continents to visualize the differences in landscapes that make geospatial generalization particularly difficult, and summarize our takeaways for future satellite imagery-related applications.