WALGREEN: Web Based Platform for Soil Organic Carbon Inference Applications

  1. Aroca, José Manuel 1
  2. Díez Pastor, José Francisco 1
  3. Latorre-Carmona, Pedro 1
  4. Canepa Oneto, Antonio 1
  5. Rad, Juan Carlos 1
  6. Camps-Valls, Gustau 3
  7. Elvira, Víctor 2
  8. García-Osorio, César 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

  2. 2 University of Edinburgh
    info

    University of Edinburgh

    Edimburgo, Reino Unido

    ROR https://ror.org/01nrxwf90

  3. 3 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

Mostra les afiliacions +
Actes:
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium

Editorial: Institute of Electrical and Electronics Engineers

ISSN: 2153-7003,  2153-6996

Any de publicació: 2024

Pàgines: 3992-3996

Tipus: Aportació congrés

DOI: 10.1109/IGARSS53475.2024.10642525 GOOGLE SCHOLAR lock_openAccés obert editor

Resum

Remote sensing data management and its use for classification and inference purposes is at the forefront of research tasks nowadays. There are, however, some inherent drawbacks and difficulties when dealing with, and understanding how satellite information is provided (particularly when referring to multiband/multispectral satellite platforms) and how different and disparate datasets related to soil content can be used and merged with this imagery.We present WALGREEN. The aim of this tool is to provide a secure environment to handle the whole process to use polygons or, geographical coordinates in tiff/geotiff images, have real-time access to images, save and get soil organic carbon real measurements, and generate datasets for machine learning training and inferential methods. We also aim to providing a framework to preprocess soil organic carbon information from different but accepted sources, like the Land Use/Cover Area frame statistical Survey database, so that even without real measurements, researchers may be able to start training different machine learning methodologies.

€ Mostra el finançament

Informació de finançament

Finançadors

Referències bibliogràfiques

  • 10.3390/rs15082118
  • 10.1016/j.rse.2018.09.015
  • 10.3390/rs15071822
  • 10.1016/j.geoderma.2020.114365
  • 10.3390/ijgi11070361
  • 10.1080/10106049.2021.1952314
  • 10.3390/rs10121927
  • 10.1016/j.isprsjprs.2018.11.026
  • 10.1016/j.scitotenv.2020.138244
  • 10.1016/j.jenvman.2023.117810
  • 10.3390/rs11060676
  • 10.1111/ejss.12499
  • 10.3390/rs13091791
  • 10.1016/j.catena.2022.106077