mlsanityData to 7 Oct 2026

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

arXiv 2401.02424 ↗First posted 6 Nov 2023cs.CV cs.AI cs.LGcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 41 references that are in the map. First posted on arXiv 6 Nov 2023.

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11 of 41 references.

References · 11

  1. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  2. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,488
  3. 2019Why gradient clipping accelerates training: A theoretical justification for adaptivitycited by 335
  4. 2017PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrievalcited by 96
  5. 2022Parameter-efficient Model Adaptation for Vision Transformerscited by 81
  6. 2020Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunitiescited by 71
  7. 2020An Attention-Based System for Damage Assessment Using Satellite Imagerycited by 11
  8. 2021Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative Studycited by 9
  9. 2019Deep Discriminative Representation Learning with Attention Map for Scene Classificationcited by 8
  10. 2019Style transfer-based image synthesis as an efficient regularization technique in deep learningcited by 8
  11. 2019Adaptive Learning Rate Clipping Stabilizes Learningcited by 6

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