Corpus ID: 218502664

LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery

@article{Boguszewski2020LandCoveraiDF,
  title={LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery},
  author={Adrian Boguszewski and D. Batorski and Natalia Ziemba-Jankowska and Anna Zambrzycka and T. Dziedzic},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.02264}
}
  • Adrian Boguszewski, D. Batorski, +2 authors T. Dziedzic
  • Published 2020
  • Computer Science
  • ArXiv
  • Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable the precise assessment and can significantly speed up the process of change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However… CONTINUE READING

    References

    SHOWING 1-10 OF 43 REFERENCES
    Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis
    • 11
    • Highly Influential
    • PDF
    Machine learning for aerial image labeling
    • 252
    • Highly Influential
    • PDF
    AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification
    • 441
    • PDF
    Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle
    • 59
    Large Scale High-Resolution Land Cover Mapping With Multi-Resolution Data
    • Caleb Robinson, L. Hou, +4 authors Nebojsa Jojic
    • Computer Science, Environmental Science
    • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    • 2019
    • 17
    • PDF
    Learning Aerial Image Segmentation From Online Maps
    • 106
    • PDF
    Object based image analysis for remote sensing
    • 3,135
    • PDF
    Tree Cover for the Year 2010 of the Metropolitan Region of São Paulo, Brazil
    • 1
    • PDF
    iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images
    • 13
    • PDF