This document summarizes research on detecting grassland mowing events using satellite imagery. The researchers used Sentinel-1 radar and Sentinel-2 optical data to create dense time series datasets for regions in Lithuania. They developed a fusion model to combine the datasets and overcome issues like cloud coverage. A deep learning algorithm was then used to identify mowing events, including cases with large data gaps. The results showed good detection of mowing compliance based on national regulations. Future work aims to improve the models and generalize the methods to other event detection tasks.
NextGEOSS: The Next Generation European Data Hub and Cloud Platform for Earth...Wolfgang Ksoll
NextGEOSS is a H2020 project that aims to create an open data hub and cloud platform for Earth observation data. It involves 27 partners from 13 countries with a budget of 10 million euros from 2016-2020. The project will develop advanced data discovery tools, enable user feedback, and enhance communities through tailored solutions. It will follow an open, inclusive, and agile development approach aligned with EU open data policies. Various pilot projects will use the data and platform for applications in agriculture, biodiversity, disaster risk reduction, and other areas. The data will come from Copernicus satellites, in situ sources, and other open data providers. Metadata will be harvested and standardized. Lessons learned so far include the need for scalable architectures
This document defines geographic information systems (GIS) and describes their key components and uses. GIS is defined as a technology that uses hardware, software and information management strategies to capture, store, analyze and display spatially-referenced data to improve decision-making. It involves a spatially-referenced computer database and applications software. GIS is unique in that it handles spatial data referenced by location, and connects activities based on spatial proximity. Common GIS applications include facilities management, environmental analysis, transportation routing, health analysis, and more.
Advances in Agricultural remote sensingsAyanDas644783
This document summarizes a 3-part training program on crop mapping using synthetic aperture radar (SAR) and optical remote sensing. The training will cover crop classification using time series of polarimetric SAR data, monitoring crop growth through SAR-derived crop structural parameters, and classifying crop types using time series optical and radar data. Attendees will learn how to analyze satellite image time series from sensors like Sentinel-1 and Sentinel-2 for applications like crop monitoring. The training objectives are to understand polarimetric SAR for crop assessment and using multitemporal SAR and optical data for crop monitoring and classification.
This document summarizes a new cloud-based platform for analyzing large satellite Earth observation (EO) data. Key points:
- Traditional desktop tools cannot handle the growing size of EO data, requiring a shift to cloud-native analytics using big data tools.
- The demonstrated platform brings algorithms to distributed data using open-source tools like Kubernetes, Docker, and Jupyter notebooks running on Google Cloud.
- It provides scalable infrastructure for processing and analyzing petabytes of EO data through pipelines, supports open data standards, and ensures data sovereignty with Canadian storage.
Beyond Seminars - Deep Learning for fusion of Sentinel-1 and Sentinel-2 data ...ENVISION H2020
Iason Tsardanidis presents in the BEYOND Centre his work on ENVISION H2020 project regarding Deep Learning for fusion of Sentinel-1 and Sentinel-2 data and grassland mowing detection to promote peer-to-peer learning between the various teams of BEYOND!
Artificial Intelligence in the Earth Observation Domain: Current European Res...ExtremeEarth
The document summarizes four European projects applying artificial intelligence to earth observation data: ExtremeEarth, AI4Copernicus, DeepCube, and Callisto. ExtremeEarth develops scalable AI and big data techniques for food security and polar use cases. AI4Copernicus aims to make the AI4EU platform useful for Copernicus data users through open calls. DeepCube focuses on explainable AI for problems like drought forecasting. Callisto combines satellite and other data through digital twins to support sustainable development. The projects emphasize distributed deep learning, semantic technologies, cloud/edge computing, and high-impact use cases.
This document summarizes research on detecting grassland mowing events using satellite imagery. The researchers used Sentinel-1 radar and Sentinel-2 optical data to create dense time series datasets for regions in Lithuania. They developed a fusion model to combine the datasets and overcome issues like cloud coverage. A deep learning algorithm was then used to identify mowing events, including cases with large data gaps. The results showed good detection of mowing compliance based on national regulations. Future work aims to improve the models and generalize the methods to other event detection tasks.
NextGEOSS: The Next Generation European Data Hub and Cloud Platform for Earth...Wolfgang Ksoll
NextGEOSS is a H2020 project that aims to create an open data hub and cloud platform for Earth observation data. It involves 27 partners from 13 countries with a budget of 10 million euros from 2016-2020. The project will develop advanced data discovery tools, enable user feedback, and enhance communities through tailored solutions. It will follow an open, inclusive, and agile development approach aligned with EU open data policies. Various pilot projects will use the data and platform for applications in agriculture, biodiversity, disaster risk reduction, and other areas. The data will come from Copernicus satellites, in situ sources, and other open data providers. Metadata will be harvested and standardized. Lessons learned so far include the need for scalable architectures
This document defines geographic information systems (GIS) and describes their key components and uses. GIS is defined as a technology that uses hardware, software and information management strategies to capture, store, analyze and display spatially-referenced data to improve decision-making. It involves a spatially-referenced computer database and applications software. GIS is unique in that it handles spatial data referenced by location, and connects activities based on spatial proximity. Common GIS applications include facilities management, environmental analysis, transportation routing, health analysis, and more.
Advances in Agricultural remote sensingsAyanDas644783
This document summarizes a 3-part training program on crop mapping using synthetic aperture radar (SAR) and optical remote sensing. The training will cover crop classification using time series of polarimetric SAR data, monitoring crop growth through SAR-derived crop structural parameters, and classifying crop types using time series optical and radar data. Attendees will learn how to analyze satellite image time series from sensors like Sentinel-1 and Sentinel-2 for applications like crop monitoring. The training objectives are to understand polarimetric SAR for crop assessment and using multitemporal SAR and optical data for crop monitoring and classification.
This document summarizes a new cloud-based platform for analyzing large satellite Earth observation (EO) data. Key points:
- Traditional desktop tools cannot handle the growing size of EO data, requiring a shift to cloud-native analytics using big data tools.
- The demonstrated platform brings algorithms to distributed data using open-source tools like Kubernetes, Docker, and Jupyter notebooks running on Google Cloud.
- It provides scalable infrastructure for processing and analyzing petabytes of EO data through pipelines, supports open data standards, and ensures data sovereignty with Canadian storage.
Beyond Seminars - Deep Learning for fusion of Sentinel-1 and Sentinel-2 data ...ENVISION H2020
Iason Tsardanidis presents in the BEYOND Centre his work on ENVISION H2020 project regarding Deep Learning for fusion of Sentinel-1 and Sentinel-2 data and grassland mowing detection to promote peer-to-peer learning between the various teams of BEYOND!
Artificial Intelligence in the Earth Observation Domain: Current European Res...ExtremeEarth
The document summarizes four European projects applying artificial intelligence to earth observation data: ExtremeEarth, AI4Copernicus, DeepCube, and Callisto. ExtremeEarth develops scalable AI and big data techniques for food security and polar use cases. AI4Copernicus aims to make the AI4EU platform useful for Copernicus data users through open calls. DeepCube focuses on explainable AI for problems like drought forecasting. Callisto combines satellite and other data through digital twins to support sustainable development. The projects emphasize distributed deep learning, semantic technologies, cloud/edge computing, and high-impact use cases.
Solving advanced research problems with real time open data from satellites a...Wolfgang Ksoll
The project NextGEOSS brings wit its data hub based on CKAN and its 10 pilot programs a new quality in the usage of earth observation open data from satellites and in situ.
This document provides an overview of geographical information systems (GIS) and remote sensing. It defines GIS and explains its key components, principles, functions, data types, advantages and disadvantages. It also defines remote sensing, describes its principles and stages, and outlines its applications in geology, natural resource management, national security and more. The advantages of remote sensing include large area coverage and permanent data records, while disadvantages include high costs and need for specialized training.
The document discusses big data use cases and requirements. It provides 51 detailed use cases across various domains that generate many terabytes to petabytes of data. It also describes extracting 437 specific requirements from the use cases and analyzing trends. The next steps involve matching requirements to a reference architecture and prioritizing use cases for implementation.
BigDataEurope 1st SC5 Workshop, Project Teleios & LEO, by M. Koubarakis, Univ...BigData_Europe
The TELEIOS and LEO projects developed techniques for managing and analyzing large volumes of linked open Earth observation data. Key technologies included the Strabon spatial RDF store, MonetDB SciQL for scientific queries, GeoTriples for transforming data to RDF, and Sextant for visualization. Applications included semantic catalogs, wildfire monitoring, burn scar mapping, and precision farming. Lessons indicated open EO data will grow significantly and integrating it with linked data can support many applications, requiring scientific database and semantic web technologies.
Landmap provides geospatial datasets and resources for education. It aims to increase awareness of geospatial data and provide learning materials structured into courses, units, and topics. The learning zone covers topics from basic to advanced levels and includes software, data, theory, and workflows. It has a technological framework based on Joomla and a pedagogical framework following a six-part educational model. Future plans include making some resources openly available and developing new content areas and engagement with researchers.
This document summarizes three case studies that used remote sensing and GIS techniques to analyze land use and land cover change over time. The first case study analyzed changes from 1990-2010 in Hawalbagh, India using Landsat imagery. It found increases in built-up land and decreases in barren land. The second studied coastal Egypt from 1987-2001 using Landsat, identifying 8 land cover classes. The third examined Simly watershed, Pakistan from 1992-2012 using Landsat and SPOT data, finding increases in agriculture and decreases in vegetation. All three used supervised classification and post-classification comparison to analyze land use/cover changes.
Use of satellite imagery for the generation of an aquaculture atlas : a case ...Blue BRIDGE
The document describes a project to design an Aquaculture Atlas Production System (AAPS) that takes satellite imagery as input and generates products about aquaculture for users. It will implement a prototype for areas in Greece and Indonesia. The system will use tools to automatically detect aquaculture features like fish cages in images and map them. It will make the outputs like location maps and statistics available to users through a virtual research environment by the end of 2016 to support aquaculture monitoring, analysis and planning.
A presentation given by Peter McKeague (Historic Environment Scotland), Anthony Corns (Discovery Programme, Ireland) and Axel Posluschny (University of Bamberg, Germany) at the European Archaeological Consilium annual meeting in Brighton, March 2015.
The document proposes the Farmers' Integration Platform as a Service (FIPaaS) project, which aims to integrate existing technologies for precision and sustainable farming into a single, open-source, cloud-based solution to improve farmers' livelihoods. The project is funded by the EU-Egypt PRIMA initiative and involves partners from several Mediterranean countries. It will develop tools for satellite imaging, drone data collection, smart irrigation, and other functions. The project expects to reduce water usage, increase crop yields, and disseminate technologies to farmers through its unified platform.
Artificial Intelligence and Big Data Techniques for Copernicus Data: the ExtremeEarth project
Manolis Koubarakis (Professor at the National and Kapodistrian University of Athens and Adjunct Researcher at the Institute of the Management of Information Systems)
Modern tools and techniques can help address challenges in water data management. Water data management platforms use data sharing platforms to integrate data from multiple agencies in a standardized format. These platforms incorporate a hydrological geofabric to establish a single point of truth for water mapping, and use cloud computing to provide scalable access and analysis of large water datasets. For example, a demonstration showed how sensor data, water storage data, and river flow models could be integrated in a sensor cloud to help manage water sharing in a catchment.
Sensor Networks Introduction and ArchitecturePeriyanayagiS
This document provides an overview of sensor networks and wireless sensor network architectures. It begins with an introduction to wireless sensor networks and their components. It then discusses the topics, challenges, and enabling technologies for WSNs. The document outlines the architecture of a sensor node and its goals. It provides examples of WSN applications and discusses sensor network deployment considerations. Finally, it addresses the design challenges, operational challenges, and required mechanisms for WSNs to meet their requirements.
R3 TREES - Integrated Management of Urban Green AreasPaolo Viskanic
R3 GIS is an Italian company that develops green area management software called R3 TREES. The software allows multiple stakeholders to access a central geodatabase of urban green space assets. It facilitates jobs, inspections, and workflows while also providing citizen information through public maps. R3 TREES supports management of various asset types and integrates tools for data entry, quality control, historical records, and more to help municipalities efficiently maintain their urban green areas.
Artificial Intelligence and Big Data Technologies for Copernicus Data: the Ex...ExtremeEarth
The ExtremeEarth Project developed artificial intelligence and big data techniques to analyze large volumes of Copernicus Earth observation data. Key aspects included developing deep learning models for crop mapping and sea ice classification using large training datasets. Technologies were integrated into the Hopsworks platform and deployed on CREODIAS to enable distributed deep learning at extreme scales for food security and polar use cases. The goal was to make these techniques and platforms openly available to support the European EO industry.
La presentazione del Progetto SmartGeo a cura di Guido Satta, in occasione dell'evento "Bonifiche ambientali e potenzialità delle imprese" che si è tenuto a Cagliari il 7 novembre 2014.
This presentation was given by Prof. K N Subramanya, Principal, RV College of Engineering & CoE IoT during IoTForum's AgriTech Day 2019 on February 9, 2019 at NIANP-ICAR, Bangaluru
ExtremeEarth is a H2020 project that aims to develop extreme data analytics techniques using big Copernicus data and apply these technologies to food security and polar use cases. The project involves 11 partners from 7 countries and has a budget of ~6 million Euro over 36 months. It will integrate artificial intelligence and deep learning methods to extract information from Copernicus satellite imagery for applications in monitoring crop growth/yield and sea ice conditions. The food security use case will generate water availability maps for irrigation management while the polar use case focuses on automated regional sea ice charts for maritime safety.
Viene descritta la piattaforma EiAGRID/SmartGeo, un portale di calcolo e analisi dati per sismica a riflessione e acquisizioni GPR multioffset, che mette a disposizione dell'utente una serie di servizi di calcolo e di processing accessibili attraverso un'interfaccia Web basata su un'infrastruttura Grid. La piattaforma consente all'utente in campo, tramite un dispositivo client (laptop, PC, tablet, etc.), di usufruire di una serie di servizi computazionali che risiedono e girano su server remoti, secondo il paradigma SaaS (Software as a Service). Verranno illustrate le soluzioni modellistiche e tecnologiche adottate e alcuni risultati ottenuti su dati reali.
This document summarizes the objectives, partners, and services of the SERVIR Network, which is a regional partnership that uses earth observation data and geospatial technologies to address issues related to food security, water resources, weather and climate, and land use in Southeast Asia. The SERVIR Network aims to build capacity for using geospatial data and tools, improve access to this information, and support decision-making. It develops products and services through stakeholder engagement and open data sharing. A needs assessment identified priorities like land cover mapping, early warning systems, water resources management, and crop forecasting. The document describes datasets and tools developed by SERVIR, including a dam inundation areas dataset and online and desktop tools for modeling reservoir areas
An automated end-to-end framework for CAP monitoring, On-demand access to the...ENVISION H2020
This document discusses an application that was developed to unlock the power of Earth observation big data for users by allowing them to request data from an EO data cube via an interface or API. The requested data can be analyzed in multiple formats. The application utilizes Django and Django Ninja to create a web application and REST API. It retrieves data like Sentinel-2 bands and vegetation indices from the cube and visualizes it. The retrieved data can be used for tasks like monitoring vegetation health. The application is currently being used by CAPO to ingest requested data into their systems to enhance validation processes. There is potential to improve the application by connecting it to an ENVISION database and adding more functionality.
An automated end-to-end framework for CAP monitoring - Lessons learned from ...ENVISION H2020
This project received funding from the European Union's Horizon 2020 programme to develop EO data products and services to efficiently monitor agriculture and the environment. Machine learning models are applied to Sentinel-1 and -2 data to create dynamic crop maps and detect potential non-compliance, such as false declarations. Services include crop classification maps, alerts for irregularities, and monitoring of regulations around crop diversification, soil protection, and protected areas. The system aims to reduce fraud and errors while ensuring sustainable agricultural practices.
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Solving advanced research problems with real time open data from satellites a...Wolfgang Ksoll
The project NextGEOSS brings wit its data hub based on CKAN and its 10 pilot programs a new quality in the usage of earth observation open data from satellites and in situ.
This document provides an overview of geographical information systems (GIS) and remote sensing. It defines GIS and explains its key components, principles, functions, data types, advantages and disadvantages. It also defines remote sensing, describes its principles and stages, and outlines its applications in geology, natural resource management, national security and more. The advantages of remote sensing include large area coverage and permanent data records, while disadvantages include high costs and need for specialized training.
The document discusses big data use cases and requirements. It provides 51 detailed use cases across various domains that generate many terabytes to petabytes of data. It also describes extracting 437 specific requirements from the use cases and analyzing trends. The next steps involve matching requirements to a reference architecture and prioritizing use cases for implementation.
BigDataEurope 1st SC5 Workshop, Project Teleios & LEO, by M. Koubarakis, Univ...BigData_Europe
The TELEIOS and LEO projects developed techniques for managing and analyzing large volumes of linked open Earth observation data. Key technologies included the Strabon spatial RDF store, MonetDB SciQL for scientific queries, GeoTriples for transforming data to RDF, and Sextant for visualization. Applications included semantic catalogs, wildfire monitoring, burn scar mapping, and precision farming. Lessons indicated open EO data will grow significantly and integrating it with linked data can support many applications, requiring scientific database and semantic web technologies.
Landmap provides geospatial datasets and resources for education. It aims to increase awareness of geospatial data and provide learning materials structured into courses, units, and topics. The learning zone covers topics from basic to advanced levels and includes software, data, theory, and workflows. It has a technological framework based on Joomla and a pedagogical framework following a six-part educational model. Future plans include making some resources openly available and developing new content areas and engagement with researchers.
This document summarizes three case studies that used remote sensing and GIS techniques to analyze land use and land cover change over time. The first case study analyzed changes from 1990-2010 in Hawalbagh, India using Landsat imagery. It found increases in built-up land and decreases in barren land. The second studied coastal Egypt from 1987-2001 using Landsat, identifying 8 land cover classes. The third examined Simly watershed, Pakistan from 1992-2012 using Landsat and SPOT data, finding increases in agriculture and decreases in vegetation. All three used supervised classification and post-classification comparison to analyze land use/cover changes.
Use of satellite imagery for the generation of an aquaculture atlas : a case ...Blue BRIDGE
The document describes a project to design an Aquaculture Atlas Production System (AAPS) that takes satellite imagery as input and generates products about aquaculture for users. It will implement a prototype for areas in Greece and Indonesia. The system will use tools to automatically detect aquaculture features like fish cages in images and map them. It will make the outputs like location maps and statistics available to users through a virtual research environment by the end of 2016 to support aquaculture monitoring, analysis and planning.
A presentation given by Peter McKeague (Historic Environment Scotland), Anthony Corns (Discovery Programme, Ireland) and Axel Posluschny (University of Bamberg, Germany) at the European Archaeological Consilium annual meeting in Brighton, March 2015.
The document proposes the Farmers' Integration Platform as a Service (FIPaaS) project, which aims to integrate existing technologies for precision and sustainable farming into a single, open-source, cloud-based solution to improve farmers' livelihoods. The project is funded by the EU-Egypt PRIMA initiative and involves partners from several Mediterranean countries. It will develop tools for satellite imaging, drone data collection, smart irrigation, and other functions. The project expects to reduce water usage, increase crop yields, and disseminate technologies to farmers through its unified platform.
Artificial Intelligence and Big Data Techniques for Copernicus Data: the ExtremeEarth project
Manolis Koubarakis (Professor at the National and Kapodistrian University of Athens and Adjunct Researcher at the Institute of the Management of Information Systems)
Modern tools and techniques can help address challenges in water data management. Water data management platforms use data sharing platforms to integrate data from multiple agencies in a standardized format. These platforms incorporate a hydrological geofabric to establish a single point of truth for water mapping, and use cloud computing to provide scalable access and analysis of large water datasets. For example, a demonstration showed how sensor data, water storage data, and river flow models could be integrated in a sensor cloud to help manage water sharing in a catchment.
Sensor Networks Introduction and ArchitecturePeriyanayagiS
This document provides an overview of sensor networks and wireless sensor network architectures. It begins with an introduction to wireless sensor networks and their components. It then discusses the topics, challenges, and enabling technologies for WSNs. The document outlines the architecture of a sensor node and its goals. It provides examples of WSN applications and discusses sensor network deployment considerations. Finally, it addresses the design challenges, operational challenges, and required mechanisms for WSNs to meet their requirements.
R3 TREES - Integrated Management of Urban Green AreasPaolo Viskanic
R3 GIS is an Italian company that develops green area management software called R3 TREES. The software allows multiple stakeholders to access a central geodatabase of urban green space assets. It facilitates jobs, inspections, and workflows while also providing citizen information through public maps. R3 TREES supports management of various asset types and integrates tools for data entry, quality control, historical records, and more to help municipalities efficiently maintain their urban green areas.
Artificial Intelligence and Big Data Technologies for Copernicus Data: the Ex...ExtremeEarth
The ExtremeEarth Project developed artificial intelligence and big data techniques to analyze large volumes of Copernicus Earth observation data. Key aspects included developing deep learning models for crop mapping and sea ice classification using large training datasets. Technologies were integrated into the Hopsworks platform and deployed on CREODIAS to enable distributed deep learning at extreme scales for food security and polar use cases. The goal was to make these techniques and platforms openly available to support the European EO industry.
La presentazione del Progetto SmartGeo a cura di Guido Satta, in occasione dell'evento "Bonifiche ambientali e potenzialità delle imprese" che si è tenuto a Cagliari il 7 novembre 2014.
This presentation was given by Prof. K N Subramanya, Principal, RV College of Engineering & CoE IoT during IoTForum's AgriTech Day 2019 on February 9, 2019 at NIANP-ICAR, Bangaluru
ExtremeEarth is a H2020 project that aims to develop extreme data analytics techniques using big Copernicus data and apply these technologies to food security and polar use cases. The project involves 11 partners from 7 countries and has a budget of ~6 million Euro over 36 months. It will integrate artificial intelligence and deep learning methods to extract information from Copernicus satellite imagery for applications in monitoring crop growth/yield and sea ice conditions. The food security use case will generate water availability maps for irrigation management while the polar use case focuses on automated regional sea ice charts for maritime safety.
Viene descritta la piattaforma EiAGRID/SmartGeo, un portale di calcolo e analisi dati per sismica a riflessione e acquisizioni GPR multioffset, che mette a disposizione dell'utente una serie di servizi di calcolo e di processing accessibili attraverso un'interfaccia Web basata su un'infrastruttura Grid. La piattaforma consente all'utente in campo, tramite un dispositivo client (laptop, PC, tablet, etc.), di usufruire di una serie di servizi computazionali che risiedono e girano su server remoti, secondo il paradigma SaaS (Software as a Service). Verranno illustrate le soluzioni modellistiche e tecnologiche adottate e alcuni risultati ottenuti su dati reali.
This document summarizes the objectives, partners, and services of the SERVIR Network, which is a regional partnership that uses earth observation data and geospatial technologies to address issues related to food security, water resources, weather and climate, and land use in Southeast Asia. The SERVIR Network aims to build capacity for using geospatial data and tools, improve access to this information, and support decision-making. It develops products and services through stakeholder engagement and open data sharing. A needs assessment identified priorities like land cover mapping, early warning systems, water resources management, and crop forecasting. The document describes datasets and tools developed by SERVIR, including a dam inundation areas dataset and online and desktop tools for modeling reservoir areas
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An automated end-to-end framework for CAP monitoring, On-demand access to the...ENVISION H2020
This document discusses an application that was developed to unlock the power of Earth observation big data for users by allowing them to request data from an EO data cube via an interface or API. The requested data can be analyzed in multiple formats. The application utilizes Django and Django Ninja to create a web application and REST API. It retrieves data like Sentinel-2 bands and vegetation indices from the cube and visualizes it. The retrieved data can be used for tasks like monitoring vegetation health. The application is currently being used by CAPO to ingest requested data into their systems to enhance validation processes. There is potential to improve the application by connecting it to an ENVISION database and adding more functionality.
An automated end-to-end framework for CAP monitoring - Lessons learned from ...ENVISION H2020
This project received funding from the European Union's Horizon 2020 programme to develop EO data products and services to efficiently monitor agriculture and the environment. Machine learning models are applied to Sentinel-1 and -2 data to create dynamic crop maps and detect potential non-compliance, such as false declarations. Services include crop classification maps, alerts for irregularities, and monitoring of regulations around crop diversification, soil protection, and protected areas. The system aims to reduce fraud and errors while ensuring sustainable agricultural practices.
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This document summarizes CAPO's involvement in the ENVISION project, which aims to develop new approaches for checks and monitoring under the New Common Agricultural Policy (CAP). It describes CAPO joining in 2020 when no EU legislation or Cyprus Strategic Plan existed. The project opportunities for adjusting to needs before the 2023 transitional period. Services of interest include crop classification, vegetation/soil indexing, and monitoring cross compliance. The document outlines validation methods and challenges addressed through data exchange and testing toward accurate results. It presents the datacube endpoint service providing on-demand access and a mobile app for geotagged photos to facilitate monitoring.
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National Observatory of Athens
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This document summarizes work done by the National Observatory of Athens (NOA) for the ENVISION project. NOA developed solutions using Earth observation data from Sentinel satellites to monitor the Common Agricultural Policy (CAP) in Lithuania and Cyprus at national scales. Key solutions included multi-temporal crop type maps, grassland mowing event detection, and compliance monitoring maps. The solutions were implemented using a datacube to store and process satellite imagery at scale. The results supported CAP authorities' decision making and monitoring efforts.
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This document discusses the use of satellite imagery and machine learning to monitor organic farming practices and help certification bodies conduct inspections. Currently, inspections are costly and time-consuming due to sampling-based on-farm checks. The proposed system would use satellite data on crop growth patterns combined with farmer declarations to continuously monitor all farmland and potentially replace on-site inspections, reducing costs and administrative burden for certifying organic compliance. Machine learning models are trained to distinguish organic and conventional farming signatures using historical satellite and inspection data, and could provide warnings through a traffic light system.
This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. A legal notice states that the views expressed are solely those of the author and not the responsibility of the EASME. The document was authored by Vassilis Spitadakis of ETAM SA on October 12, 2022.
ENVISION: Steps towards developing innovative soil services at a regional sca...ENVISION H2020
The document discusses a project by ILVO, the Flanders Research Institute for Agriculture, Fisheries and Food, to develop a service for estimating soil organic carbon levels in cropland using satellite imagery. The project aims to simplify monitoring of soil carbon for Common Agricultural Policy requirements. ILVO is collecting soil samples and Sentinel-2 satellite imagery to build models correlating image data with soil carbon measurements. The models will map soil carbon across Flanders to reduce on-site inspections and administration. ILVO is improving their methods by addressing limitations and directly using time series satellite data in their models.
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This document discusses using artificial intelligence and different data sources to improve monitoring of the Common Agricultural Policy (CAP). It proposes moving from smart sampling of on-the-spot checks (OTSCs) based on satellite imagery to exhaustive wall-to-wall monitoring using additional data like very high resolution satellite imagery, unmanned aerial vehicles, and geotagged street-level photos. Methods are described for collecting and annotating street-level images and combining them with satellite data for improved crop classification models. Future work is outlined on image quality assessment, semantic segmentation to identify agriculture, creating benchmark datasets, and exploring data fusion models.
ENVISION Coproducing Earth Observation based monitoring tools for sustainable...ENVISION H2020
The document describes the ENVISION project which aims to develop tools for monitoring agricultural practices using earth observation. It received funding from the European Union Horizon 2020 program. The project uses a coproduction approach involving stakeholders from different sectors to develop solutions iteratively. This includes identifying needs, generating ideas, prototyping tools, and testing them with feedback. The goal is to ensure the tools effectively address key challenges in a way that is sustainable beyond the project's lifetime.
This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 869366. The project involves partners from Greece, Serbia, Lithuania, UK, Slovenia, Belgium, and Cyprus. It aims to develop a mobile application and Earth observation-derived services to assist in remotely monitoring farmers' compliance with environmental practices, helping to address issues like water pollution, soil degradation, and GHG emissions. The services will work to automate wide-area monitoring throughout the year at lower cost while increasing transparency for public authorities.
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If you want to check how many SIM cards are registered under your name, you can do it easily. Just go to your mobile network provider’s website or app. Look for the feature called “SIM Ownership CNIC Tracker.” Then, type in your CNIC number correctly. After you submit it, the system will show you a list of all the SIM cards registered under your name. It will tell you which ones are active (in use) and which ones are inactive (not in use). Check this list carefully to see if there are any SIM cards you don’t need anymore. If you find any inactive ones, you can remove them to make room for new ones. This is helpful if you’re trying to add a new SIM card but all the slots are full. If you have any questions or problems with the registered SIM cards, you can contact your mobile network provider’s customer support for help.. By doing this, you can manage your SIM cards better and make sure you’re using your slots efficiently.
What information does live tracker provide for CNIC numbers?
SimOwnerDetails.online offers a comprehensive range of NADRA sim owner details for CNIC numbers. This includes the holder’s name, address, and a complete list of mobile numbers registered under the CNIC. Users can access detailed information about each registered SIM, facilitating better management and security of their telecommunications accounts.
What Sim information does SimOwnerDetails.online provide for SIM card numbers?
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Dubai is home to numerous advanced material testing labs, offering state-of-the-art facilities for a wide range of industries. These labs provide critical services such as mechanical testing, chemical analysis, and non-destructive testing, ensuring the quality and durability of materials used in construction, aerospace, and manufacturing.
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Whatsapp Number For Paid Service:
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The CNIC Information System is a comprehensive database managed by the National Database and Registration Authority (NADRA) of Pakistan. It serves as the primary source of identification for Pakistani citizens and residents, containing vital information such as name, date of birth, address, and biometric data.
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An electrical testing lab in Dubai plays a crucial role in ensuring the safety and efficiency of electrical systems across various industries. Equipped with state-of-the-art technology and staffed by experienced professionals, these labs conduct comprehensive tests on electrical components, systems, and installations.
Analytics on Vegetation & Soil Index time-series and DataCube End Point service
1. Analytics on Vegetation & Soil Index
time-series and DataCube End Point
service
Vassilis Sitokonstantinou, Mariza Kaskara, Iason Tsardanidis,
Thanassis Drivas, Alexandros Marantos, Alkis Koukos, Haris Kontoes
AgriHUB | Agriculture, Ecosystems and Environment Group
25-10-2022
2. Contents 01. The need of
geospatial analytics
03. Analytics on
Vegetation and Soil
Index Time-series
02. Analytics
functionalities
04. On-Demand
Access to the data
3. The possibility of
managing and
processing geospatial
big data to help
decision-making
therefore appears to be
an important scientific
and societal issue
01. Geospatial Analytics
However, it is difficult to
store, manage, process,
analyse, visualize and
extract useful information,
trends and knowledge
from geospatial big data
using traditional
approaches on local
machines.
4. • As ENVISION Data Cube is hosted on CreoDIAS, it has direct access to the full archive of Sentin
el images, without the need for downloading them.
• Automated and scheduled pipelines search for new satellite images, pre-process them and
generate resampled and re-projected Sentinel-1 and Sentinel-2 Cloud Optimized GeoTIFFs
• Metadata of these files are indexed to a PostgreSQL, while actual data are stored in CreoDIAS
VM
01. Geospatial Analytics
The DataCube Solution
5. ▪ All the required raw data
or indices are calculated
on a time window.
▪ The statistics of the
selected bands can be
provided either in the
form of aggregated
values for a parcel or as
a plot for a larger area.
Temporal Statistics
over an area
02. Analytics Functionalities
Visualizations can be extracted for any s1/s2 product, any aggregated statistic measurement, either
on the pixel or on the parcel level and for a specific parcel, a crop type or a crop family.
It plays a crucial role to the validation of results generated
by the rest of back- end processes
6. ▪ The functionality
considers the mean
value of cloud-free
pixels inside an area.
▪ As there can be gaps
between two or more
calculations due to
cloud presence, a
smoothing process
takes place.
▪ Thus, patterns can be
more noticeable and
reveal trends throughout
the years.
Smoothing
02. Analytics Functionalities
7. • This sub-task focus on
understanding possible
anomalies to any chosen
index.
• For example, NDVI
measures the greenness of
plant leaves, which
indicates an overall
vegetative health.
• As we have dense
measurements for NDVI,
there is the potential of
comparing current NDVI
value, either for a day or for
a month, to the average
computed NDVI over one or
more years.
Index Anomalies
02. Analytics Functionalities
8. Minimum Soil Cover
• GAEC 4 of current CAP demands the identification of
soil coverage during specific months throughout the
year.
• Initially, the average slope for each parcel has been
calculated based on a 20m raster Digital Elevation
Model.
• This slope refers to the full polygon, without using any
buffer zone.
• The algorithm of Geospatial Soil Sensing System
(GEOS3) has been utilized for creating soil masking
rules.
• GAEC 1 aims at the reduction of water pollution in nitra
te vulnerable areas has been developed, taking into
account the proximity into the closest water areas
• It relies on long-standing concept of soil erosion by
water, modelled through RUSLE.
• In addition, it utilizes also the orientation of each parcel
Runoff Risk Assessment
03. Analytics on Vegetation
and Soil Index Time-series
9. Stubble Burning Identification
• Through the identification of burnt crop parcels a
nd the date they were burnt, paying agencies can
monitor GAEC 6 compliance for each parcel.
• Farmers that follow this practice almost never dec
lare this action
• Pixel-based approach using dNBR index along
with sliding windows for national-scale coverage
• Checking pair of images for time instances t, t+1
and t, t+2.
• Burn ration for pixels falling into each parcel,
along with Burn Severity
03. Analytics on Vegetation and Soil Index Time-series
10. Natura 2000 Hotspot Detection
• Natura 2000 is a network of protected areas in the European Union aiming to assure the long-term survival of
Europe’s most valuable and threatened species and habitats.
• Same methodology as Stubble Burning, but using NDVI index.
• Marked pixels that fall inside Natura region and not matched with declared parcels from an LPIS are related to illegal
activity.
03. Analytics on Vegetation and Soil Index Time-series
11. API Service
• Data can be retrieved directly from the
datacube in the form of plots and
graphics.
• Users have the potential to construct
requests based on certain parameters
such as:
• Parcel ID
• Cloud Coverage
• Index
• Time range
• Buffer Zone
04. On-Demand Access to the data
…/api/parcels/{id}/{starting_date}/{ending_date}/{band}/{buffer}/{cfp}
12. API Service for raw data
• Data scientists and developers may need directly access to the data instead of getting images.
• Data Cube can be opened to authenticated users so to provide multidimensional data via xarrays for
a requested area, date range and series of bands or/and indices.
• This data are offered via the xpublish. Xpublish lets you easily publish Xarray Datasets via a REST API.
• Under the hood, Xpublish is using FastAPI.
• Efficient, on-demand delivery of large datasets may be enabled with Dask on the server-side.
04. On-Demand Access to the data