rsdtlib: Remote sensing with deep-temporal data library

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

rsdtlib: Remote sensing with deep-temporal data library

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

rsdtlib: Remote sensing with deep-temporal data library

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

SoftwareX

ISSN

2352-7110

e-ISSN

2352-7110

Svazek periodika

22

Číslo periodika v rámci svazku

May

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

6

Strana od-do

—

Kód UT WoS článku

000960463100001

EID výsledku v databázi Scopus

2-s2.0-85150427470