rsdtlib: Remote sensing with deep-temporal data library

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F23%3A10252101" target="_blank" >RIV/61989100:27740/23:10252101 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.softx.2023.101369" target="_blank" >https://doi.org/10.1016/j.softx.2023.101369</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.softx.2023.101369" target="_blank" >10.1016/j.softx.2023.101369</a>

Result language

angličtina

Original language name

rsdtlib: Remote sensing with deep-temporal data library

Original language description

For over a decade, satellite based remote sensing data have been intensively used for Deep Learning (DL) to help to identify Land Cover (LC) and Land Use (LU), and to detect urban and vegetation changes. Usually, these tasks are carried out with few samples or short and low-dimensional time series. In a recent study demonstrating urban change detection and monitoring, a windowed high-dimensional large time series (deep-temporal) was leveraged that not only considered a large amount of observations but also combined multiple modes for a higher temporal resolution. The software used in this approach for pre-processing, called rsdtlib, is described in the underlying work. It is made available to help others in the field of remote sensing to use this approach for Deep and Machine Learning (ML) solutions. The software is scalable to support a wide range of demands, including providing single observation samples, observation pairs, multiple modes, and the construction of windowed deep-temporal time series. Its output data is in a DL/ML training ready format and the software solution integrates well with existing remote sensing tools and services. The rsdtlib software is hosted on Github as an open source project to invite other researchers and practitioners in the remote sensing domain to utilize it.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

—

Continuities

—

Publication year

2023

Confidentiality

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

Name of the periodical

SoftwareX

ISSN

2352-7110

e-ISSN

2352-7110

Volume of the periodical

22

Issue of the periodical within the volume

May

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

6

Pages from-to

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UT code for WoS article

000960463100001

EID of the result in the Scopus database

2-s2.0-85150427470