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CatLC: Catalonia Multiresolution Land Cover Dataset.

Carlos García1,2, Oscar Mora3, Fernando Pérez-Aragüés1

  • 1Institut Cartogràfic i Geològic de Catalunya, Barcelona, 08038, Spain.

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This summary is machine-generated.

Researchers developed the Catalonia Multiresolution Land Cover Dataset (CatLC), a new remote sensing resource. This dataset aids machine learning in land cover mapping and analysis for various environmental applications.

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Machine Learning

Background:

  • Large annotated image datasets are crucial for object recognition progress.
  • Remote sensing lacks comprehensive, annotated datasets compared to natural images.
  • Existing remote sensing datasets are often limited in area and annotation richness.

Purpose of the Study:

  • Introduce the Catalonia Multiresolution Land Cover Dataset (CatLC).
  • Provide a richly annotated remote sensing dataset for a mid-size geographical area.
  • Facilitate research in land cover mapping and analysis using machine learning.

Main Methods:

  • Compiled pre-processed aerial and satellite imagery from ICGC and ESA.
  • Integrated detailed topographic layers from various sensors.
  • Annotated the dataset with a diverse range of land cover classes.

Main Results:

  • The CatLC dataset offers multiresolution, multimodal, and multitemporal data.
  • Includes data from aircraft and satellites, alongside topographic information.
  • Covers a significant geographical area with extensive land cover annotations.

Conclusions:

  • CatLC is a valuable resource for the machine learning community.
  • Enables exploration of new classification techniques for land cover mapping.
  • Stimulates research at the intersection of computer vision and remote sensing.