PerceptiveSentinel: list of downloadable deliverables.
| title and desprition
| type |
last update |
EO-data collection
This task comprises of collecting EO time series data (SENTINEL-1, SENTINEL-2, SENTINEL-3, LANDSAT, ENVISAT, MODIS, Planet and RapidEye) to support development, validation and demonstration activities.
Time series will be collected: (1) historical data, where available in a time span supported by non-EO and in-situ data, (2) up-to date time-series as they are available.
Programme: H2020-EU.2.1.6.3.;H2020-EU.2.1.6.1.2. - Topic(s): EO-2-2017
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Other |
2019-11-20 |
Heterogeneous Data Pre-Processing for Stream Mining
JSI will be supported by RS experts (GEOVILLE and MAGELLIUM) and agriculture experts (AIS, L&F) to deliver deep data understanding, required for the development of coherent time-features.
One of the main PerceptiveSentinel focuses will be to use biophysical indices as main input to develop TIME features.
We intend to use different (streaming) aggregates, such as moving or exponential moving average, minimum, maximum, histogram, correlation, variance, sum and others. Streaming aggregates can be applied for different time-windows.
Spatial, radiometric and time-series features of EO data will be supplemented by domain specific “agriculture†features to form a feature set – a list of attributes to enter the evaluation through data mining modelling.
Streamline modelling and experimenting will be performed by the employment of standard JSI tools to evaluate learning feature set.
Feature extractors will be developed by JSI. Their functionality is to receive Stage-1, Stage-2 and non-EO data from PerceptiveSentinel platform and to automatically extract features to be used in learning and operational phases of streaming processing.
Programme: H2020-EU.2.1.6.3.;H2020-EU.2.1.6.1.2. - Topic(s): EO-2-2017
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Other |
2019-11-20 |
Data management plan
First version of Data Management Plan (DMP) will be written in month 2.
During the project, DMP will be regularly updated and in the project after-life it will become a part of PerceptiveSentinel Policy document.
Main features addressed through DMP Will be:
- Data sharing: data sharing policies will follow rules of Open Research Data Pilot, providing an open access to research data generated through this project. Privacy and data ownership will be accounted as well. Following principles will apply: (1) data generated through project research activities will be openly available, (2) all the data which is available free of cost (for instance SENTINEL data) will be available at the same terms also through PerceptiveSentinel platform and (3) all of the other data will be (or not be) available on the terms set by data owner. The only exception of this rule is represented in PerceptiveSentinel’s DEMO REGION, where we will tend to provide ALL data free-of-charge (following special agreements with data owners). The described principles will assure that all data, required to VERIFY project deliveries, will be openly available.
- Data format of both the EO and in-situ data: this part will describe the main characteristics of the data and their provenance. The following macro-information will be managed for each data source and data set: (1) data-set reference and name, (2) data-set description, (3) data-set scope and goal for the project.
- Data protocols: protocols will be specified to be used for data exchange within the PerceptiveSentinel system, and between the PerceptiveSentinel system and the external world. Worldwide spatial standards (OGC WMS, WFS, WCS, GeoJSON) and non-spatial (XML) standards will be used.
- Catalogue data - metadata on all available data (historical, current and future) and available EO VAS will be provided in a form of Data Catalogue, which will inform users on availability of data and EO products in their target areas. Standard metadata formats will be privileged (OGC Catalogue Services, INSPIRE where relevant), while ad-hoc ones will be considered only if strictly necessary (to describe specific EO VAS).
- Archiving and preservation; Rolling archive infrastructure (data archive, which keeps adding new data with old ones remaining available for predefined amount of time) will be implemented on CLOUD infrastructure. Data driven models will be implemented on the data-input side - automatic download of SENTINEL data will be provided for areas with active subscriptions and for PerceptiveSentinel’s DEMO REGION. The data will be stored for the whole subscription period. 3 months after the subscription expires, the data will be archived and then erased from the production database. Presentation data, meant to be used as a background data layer (Level-3A data with the global coverage) will be refreshed once a month.
Programme: H2020-EU.2.1.6.3.;H2020-EU.2.1.6.1.2. - Topic(s): EO-2-2017
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Open Research Data Pilot |
2019-11-20 |
Spatial and radiometric features
Spatial and radiometric features, useful for data mining and high level information extraction, will be defined and developed.
They Will be constructed using:
- radiometric characteristics
- spectral characteristics
- spatial or local parameters such as texture values, shape and geometric descriptors (lines detection, polygons, specific textures for cultivated fields, extraction of characteristics points – SIFT/SURF) etc.
Features will be extracted from single images from the EO data collection (SENTINEL-1, SENTINEL-2, SENTINEL-3, LANDSAT, ENVISAT, MODIS, Planet and RapidEye) but if necessary, depending on the mining algorithms, they can be computed several sources of data.
Programme: H2020-EU.2.1.6.3.;H2020-EU.2.1.6.1.2. - Topic(s): EO-2-2017
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Demonstrators, pilots, prototypes |
2019-11-20 |
EO-QMiner: Stream Mining Models for Earth Observation
Several methods will be analysed during evaluation of streaming learning models (see chapter: Streaming machine learning in Part B).
Activities will result in a set of learning models to be incorporated into EO-QMiner.
Integration between PerceptiveSentinel platform and EO-QMiner is essential integrative part of the platform, enabling:
- data exchange in both ways (platform providing learning/interpretation data, EO-QMiner providing interpreted data)
- workflow control of EO-QMiner (by platform)
- administrative control of EO-QMiner (by platform)
EO-QMiner integration layer will provide JSI\'s part of integration capabilities.
Code from JSI’s open-source repository QMiner will be used to construct EO-QMiner. Certain level of new development is envisaged in the areas:
- adaptation to streaming processing and
- incorporation of new learning technologies.
Integration and functionality testing will be performed by JSI to eliminate bugs and validate integration into PerceptiveSentinel platform.
Programme: H2020-EU.2.1.6.3.;H2020-EU.2.1.6.1.2. - Topic(s): EO-2-2017
download deliverable
|
Demonstrators, pilots, prototypes |
2019-11-20 |
