JupyTEP IDE as an Online Tool for Earth Observation Data Processing

Rapiński, Jacek; Bednarczyk, Michał; Zinkiewicz, Daniel

doi:10.3390/rs11171973

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…Environmental scientists, businesses that generate value, students, and teachers are all targeted by SNAP. SNAP includes a desktop application (SNAP Desktop) for working interactively with Earth Observation data, the Graph Processing Framework to build and run recurring workflows, a command line interface, as well as Python and Java programming interfaces [14]. As a result, SNAP can be utilized for interactive picture analysis and visualization, and processing chain automation.…”

Section: Software Usedmentioning

confidence: 99%

Mapping and Mitigating Flood Extent in Keta Municipality Using Sentinel-1 SAR: A Remote Sensing Approach for Disaster Management

Afari,

Osei,

Akandekum

et al. 2022

JIPIRS

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Floods constitute a pervasive and destructive natural disaster worldwide, with increasing frequency and severity necessitating innovative approaches for monitoring, management, and mitigation. Traditional flood assessment methods, reliant on labour-intensive field inspections during flood events, are constrained by time-consuming processes and limited accessibility to affected areas. The emergence of satellite remote sensing, offering synoptic views at unprecedented spatial resolutions and accuracies, presents an opportunity to address these limitations effectively. This research harnesses satellite remote sensing, specifically the Sentinel-1 Synthetic Aperture Radar (SAR), to map the extent of the November 07, 2021 floods within the Keta Municipality. Analyzing three strategically chosen SAR data sets acquired before, during, and after the flood event, this study not only provides precise flood extent mapping but also gains valuable insights into the dynamic nature of floods in the Keta Municipality. The investigation of this study focuses on the Keta Lagoon, revealing an inundated area of 7044.3 km2 during the November 07, 2021 floods. Beyond technical methodology, this study holds critical implications for flood mitigation and urban planning. This study presents recommendations to the Keta Municipal Assembly, emphasizing the urgency of flood mitigation measures and the necessity for relocating communities near the lagoon. In an era of climate unpredictability, this research underscores the pivotal role of remote sensing in flood disaster response and proactive urban development. By synthesizing spatial data and offering a comprehensive assessment of flood dynamics, this study highlights the potential of remote sensing as an indispensable tool for disaster management and environmental monitoring. Ultimately, it contributes to the global endeavour of enhancing resilience to an escalating flood threat.

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Section: Software Usedmentioning

confidence: 99%

Mapping and Mitigating Flood Extent in Keta Municipality Using Sentinel-1 SAR: A Remote Sensing Approach for Disaster Management

Afari,

Osei,

Akandekum

et al. 2022

JIPIRS

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“…Jupyteo is an online integrated development environment (IDE) for earth observation data processing available on a cloud platform. It was created based on an earlier project: JupyTEP IDE [25]. The current version -Jupyteo -is updated, rebuilt and is more extensive than the original -JupyTEP IDE.…”

Section: Jupyteo Idementioning

confidence: 99%

A Python Library for the Jupyteo IDE Earth Observation Processing Tool Enabling Interoperability with the QGIS System for Use in Data Science

Bednarczyk

2021

GaEE

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This paper describes JupyQgis – a new Python library for Jupyteo IDE enabling interoperability with the QGIS system. Jupyteo is an online integrated development environment for earth observation data processing and is available on a cloud platform. It is targeted at remote sensing experts, scientists and users who can develop the Jupyter notebook by reusing embedded open-source tools, WPS interfaces and existing notebooks. In recent years, there has been an increasing popularity of data science methods that have become the focus of many organizations. Many scientific disciplines are facing a significant transformation due to data-driven solutions. This is especially true of geodesy, environmental sciences, and Earth sciences, where large data sets, such as Earth observation satellite data (EO data) and GIS data are used. The previous experience in using Jupyteo, both among the users of this platform and its creators, indicates the need to supplement its functionality with GIS analytical tools. This study analyzed the most efficient way to combine the functionality of the QGIS system with the functionality of the Jupyteo platform in one tool. It was found that the most suitable solution is to create a custom library providing an API for collaboration between both environments. The resulting library makes the work much easier and simplifies the source code of the created Python scripts. The functionality of the developed solution was illustrated with a test use case.

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“…in the EO realm 57 . Additionally, DIONE is formulated with the principles of producing an interoperable, reusable, scalable, multi-user system under the framework of the Spatial Data Infrastructure (SDI) 58,59 . The integration of them in the aforementioned key-components will be described in detail, in Section 3.2.…”

Section: Overview Of Dione Projectmentioning

confidence: 99%

An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives

Karagiannopoulou¹,

Tsiakos²,

Tsimiklis³

et al. 2020

Remote Sensing for Agriculture, Ecosystems, and Hydrology XXII

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The EU-funded DIONE project (grant agreement No. 870378) offers an innovative close-to-market (TRL7) solution seeking to improve the traditional methods of agricultural monitoring. The project introduces a cloud-based Software as a Service (SaaS) system architecture, building on a fusion of novel technologies that will support the forthcoming needs of the modernized Common Agriculture Policy (CAP) and the "Greening" perspectives, with an automated area-based monitoring system. In particular, an interoperable and harmonized system is designed, connecting large volumes of Earth Observation data (Satellite, UAV, and in-situ) and user-generated highly precise geolocated data (geo-tagged photos, soil measurements, etc.). DIONE's system architecture encompasses customized and third-party frameworks, where heterogeneous and multi-source data are stored, processed and managed using Artificial Intelligence (AI) algorithms. These harmonized, curated and open accessed data are then provided as Open Geospatial Consortium (OGC)-compliant, web-service layers (WMS, WFS, and WCS). Furthermore, the proposed solution formulates a scalable, flexible, interoperable, and semantically enriched environment, taking advantage of a Spatial Data Infrastructure (SDI) framework capabilities, whilst allowing an interactive connection among different tools and components through RESTful APIs. Our approach establishes a novel, cloud-based, accurate and inexpensive agriculture monitoring solution, enabling the real-time provision of multi-source data to relevant stakeholders such as Paying Agencies, Policy Officers and Control & Certification Bodies, and other domain experts. The system architecture was formulated exploiting a codesign methodology, aiming to ensure a long-term and sustainable solution. Two large scale demonstration will take place in Lithuania and Cyprus, evaluating the system capabilities in real-life and operational conditions.

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JupyTEP IDE as an Online Tool for Earth Observation Data Processing

Cited by 10 publications

References 17 publications

Mapping and Mitigating Flood Extent in Keta Municipality Using Sentinel-1 SAR: A Remote Sensing Approach for Disaster Management

Mapping and Mitigating Flood Extent in Keta Municipality Using Sentinel-1 SAR: A Remote Sensing Approach for Disaster Management

A Python Library for the Jupyteo IDE Earth Observation Processing Tool Enabling Interoperability with the QGIS System for Use in Data Science

An integrated service-based solution addressing the modernised common agriculture policy regulations and environmental perspectives

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