The amount of data coming from imaging sensors increases steadily and a modern imaging sensor creates frames of several megapixels at a high frame acquisition rate. These imaging sensors with their large data output are mounted on spaceborne platforms, but the downlink capability of these spaceborne platforms, especially for small platforms, has not been increasing …
Traditional empirical and analytical Earth Observation (EO) algorithms retrieving physical parameters are getting to a fundamental change where learning algorithms without any prior background will be able to set themselves through the ingestion of Inputs/Outputs training datasets. Nowadays, Deep Learning (DL) networks among many other Machine Learning (ML) techniques are accurate enough, and computation technology …
Recent years have brought tremendous advancements in the area of automated information extraction from Earth Observation (EO) imagery, but problems still remain since even state-of-the-art algorithms based on imagery alone do not provide a satisfactory solution. In these situations, it is possible to exploit the crowdsourcing of human intelligence, which is a recent promising area …
EO derived information are increasingly being used as the basis for a range of sensitive decisions linked to commercial operations, public safety and environmental security. At the same time, developments in ICT capability enable an expanded volume of information to be generated using distributed approaches such as cloud based storage and processing and platform based …
At present NGOs/CSOs have limited expertise in accessing and utilizing EO data. This project is working with NGOs adressinghuman rights concerns and will develop methodologies for integrating in-situ (citizen data collection), drone and EO data to enhance the collection of information and evidence on activities affecting human rights in developing countries
The project prototyped a prototype of a processing workflow for improved Sentinel-1 backscatter data, providing a temporal stack of analysis ready data (ARD) that can be integrated into a data cube system allowing to access the data in a spatial and temporal domain.
In this instalment of the webinar series, we will take a look at the process of integrating your own processor. We will go through the possibilities with respect to where to deploy your processor, how do you access the data available via UTEP and how do you retrieve the results of your processing. Join via …
The European Space Agency (ESA) is organising a Φ-week event, 9-13 September 2019, focusing on EO Open Science and FutureEO – to review the latest developments in Open Science trends. Come and hear about the latest trends in EO! The Φ-week will include a variety of events (e.g. inspiring talks, workshops, roundtables, startup pitch, hackathons) to …
The ESA Phi Week is starting next week: https://phiweek.esa.int/ We want to hear about your ideas & opinions already now – Please go to Slido.com and enter the event code for two of the main thematic blocks: AI4EO: #phiweek-AI4EO Research Infrastructures: #phiweek-RINFRA EoNext: #phiweek-EONext
In this instalment of the webinar series, we will take a look at the processors prepared by the Urban TEP consortium. Subsetting of products available via the TEP Urban platform Generation of the TimeScan from S2 and Landsat data Zonal statistics for regions based on the TEP urban layers. As a part of this, we …
