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ERCNN-DRS Urban Change Monitoring

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F21%3A10248384" target="_blank" >RIV/61989100:27740/21:10248384 - isvavai.cz</a>

  • Result on the web

    <a href="https://github.com/it4innovations/ERCNN-DRS_urban_change_monitoring" target="_blank" >https://github.com/it4innovations/ERCNN-DRS_urban_change_monitoring</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    ERCNN-DRS Urban Change Monitoring

  • Original language description

    This project contains the Ensemble of Recurrent Convolutional Neural Networks for Deep Remote Sensing (ERCNN-DRS) used for urban change monitoring with ERS-1/2 &amp; Landsat 5 TM, and Sentinel 1 &amp; 2 remote sensing mission pairs. It was developed for demonstration purposes (study case) in the ESA Blockchain ENabled DEep Learning for Space Data (BLENDED) project. Two neural network models were trained for the two eras (ERS-1/2 &amp; Landsat 5 TM: 1991-2011, and Sentinel 1 &amp; 2: 2017-2021). The enclosed data was used for the MDPI Remote Sending publication Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data [2].

  • Czech name

  • Czech description

Classification

  • Type

    R - Software

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

    <a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Internal product ID

    017/15-12-2021-SW

  • Technical parameters

    Directories ERS12_LS5 and Sentinel1_2 contain the respective: - AoI shape files used for the project (three sites: Rotterdam, Liege and Limassol) - Pre-processing script (two steps) to create the training/validation TFRecord files used for training the neural network and for validation purposes - The description of how the synthetic and noisy labels are created for supervised training - The neural network architecture model - The training script for training the neural network with the pre-processed training data; next to the training scripts are also the snapshots of the pre-trained model used in our work Directory “external” contains a (modified) 3rd party component (Omnibus change detection) under MIT license.

  • Economical parameters

    roční zvýšení objemu výroby, zisku, exportu

  • Owner IČO

    61989100

  • Owner name

    VŠB-TUO