# Mapping of Land Use and Land Cover (LULC) using EuroSAT and Transfer Learning

arXiv: https://arxiv.org/abs/2401.02424 · HTML: https://mlsanity.com/paper/2401.02424/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 6 Nov 2023.

## Builds on (most cited first, 11 of 11)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Why gradient clipping accelerates training: A theoretical justification for adaptivity](https://mlsanity.com/paper/1905.11881/) (2019)
- [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://mlsanity.com/paper/1706.03424/) (2017)
- [Parameter-efficient Model Adaptation for Vision Transformers](https://mlsanity.com/paper/2203.16329/) (2022)
- [Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities](https://mlsanity.com/paper/2005.01094/) (2020)
- [An Attention-Based System for Damage Assessment Using Satellite Imagery](https://mlsanity.com/paper/2004.06643/) (2020)
- [Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Study](https://mlsanity.com/paper/2110.02580/) (2021)
- [Deep Discriminative Representation Learning with Attention Map for Scene Classification](https://mlsanity.com/paper/1902.07967/) (2019)
- [Style transfer-based image synthesis as an efficient regularization technique in deep learning](https://mlsanity.com/paper/1905.10974/) (2019)
- [Adaptive Learning Rate Clipping Stabilizes Learning](https://mlsanity.com/paper/1906.09060/) (2019)

## Abstract (arXiv)

As the global population continues to expand, the demand for natural resources increases. Unfortunately, human activities account for 23% of greenhouse gas emissions. On a positive note, remote sensing technologies have emerged as a valuable tool in managing our environment. These technologies allow us to monitor land use, plan urban areas, and drive advancements in areas such as agriculture, climate change mitigation, disaster recovery, and environmental monitoring. Recent advances in AI, computer vision, and earth observation data have enabled unprecedented accuracy in land use mapping. By using transfer learning and fine-tuning with RGB bands, we achieved an impressive 99.19% accuracy in land use analysis. Such findings can be used to inform conservation and urban planning policies.