Category: Philab

Φ-lab hosted an internal bootcamp on Machine Learning (ML) for Earth Observation during the first week of January, which provided the team with further hands-on experience on machine learning applicability to remote sensing using Matlab. The bootcamp was led by Prof. David Lary who also led the teem to look forward to future applications of ML such as swarm intelligence.

Machine Learning and Holistic Earth Observation

Machine learning has found many applications in remote sensing. These applications range from retrieval algorithms to bias correction, from code acceleration to detection of disease in crops, from classification of pelagic habitats, to rock type classification. As a broad subfield of artificial intelligence, machine learning is concerned with algorithms and techniques that allow computers to ‘learn by example. The major focus of machine learning is to extract information from data automatically by computational and statistical methods. Over the last decade there has been considerable progress in developing a machine learning methodology for a variety of Earth Science applications involving trace gases, retrievals, aerosol products, land surface products, vegetation indices, and most recently, ocean applications.

ɸ-lab will welcome three visiting fellows from Airbus, joining a team working focused on the edge of disruptive technologies that monitor Earth’s environment from space. This is in line with one of ESA’s ɸ-lab goal: to help space researchers and companies to adopt these disruptive new technologies and methods.

Find more about this new agreement signed by Josef Aschbacher, Head of ESA’s Earth Observation Programmes, and Evert Dudok, Executive Vice President of Communications  Intelligence and Security for Airbus here.

ɸ-Lab fellowships

There are several ways and schemes through which one can work with ɸ-lab. Find more about research and visiting fellowships here.

The Φ-lab was part of a team that developed a citizen science-based application to classify ESA’s Sentinel data for machine learning in the Copernicus Space App Camp 2018 (check the Space App Camp video here!).  The mobile app, SentinelSwipe, was developed in the 1 week long Space App Camp, which brings together developers to create innovative apps that make Copernicus EO data available to a wide range of citizens and applications.

As part of a diverse team of programmers, citizen science and remote sensing experts, SentinelSwipe was developed to distribute the difficult task of providing labelled training data to the crowd, thus contributing towards advancing Earth Observation research by providing batches of classified satellite images. By using SentinelSwipe to classify and tag satellite imagery obtained from the Sentinel missions, the needed seed data to input into machine learning systems is provided to make predictions that can automate satellite image processing. 

The app repository of SentinelSwipe is hosted on our Φ-lab GitHub page.

Post contributed by Jennifer Adams.

Archaeological Prospection using Crowdsourcing and Machine Learning

Research is underway on the combined use of crowdsourcing and machine learning with EO data to systematically detect buried archaeological structures. Crowdsourcing is initially used to create labelled data, taking advantage of human interpretation to identify, often very faint, archaeological crop patterns in remotely sensed imagery. This labelled data will then be used to train a convolutional neural network to systematically identify similar buried structures over a larger area.

As the population of the Earth increases, so does the demand for resources. Development puts at risk the irreplaceable archaeological record. Especially in countries with a rich archaeological heritage, such as in the Mediterranean region, methodologies are sorely needed to increase the efficiency and reduce the costs of archaeological survey.

Participate in the crowdsourcing project to help identify buried archaeological features!

Post contributed by Chris Stewart

Copernicus programme, the largest single  Earth Observation program to date is delivering an unprecedented wealth of data imagery. In fact, during the ESA Earth Observation Φ-week, the director of ESA Earth Observation programmes Josef Aschabacher, announced that Sentinel missions are delivering 150 TB of satellite data per day! Cloud computing platforms and Artificial Intelligence (AI) are overtaking traditional tools to tackle challenges coming from Big Data and extraction of meaningful information taking advantage of high revisit time provided by these missions.

Research is underway to identify  Machine Learning (ML) techniques for classification purposes fusing satellite data of different nature, namely synthetic aperture radar (SAR) and multispectral data from Sentinel-1 and Sentinel-2, respectively, from Copernicus program. Google Earth Engine provides many ML algorithms and satellite imagery data, being the method, which are very useful for extracting land cover from different sources of imagery. One of the main goals is to understand and quantify/qualify the improvement and accuracy of the best performing model (given by its overall accuracy) considering different bands and indices scenarios for multi-temporal land cover classification.

This will allow to create in a cost-effective way large-scale maps of land-cover at high resolutions, taking advantage of completely different type of sensors and higher revisit time from merging data from these constellations.

Post contributed by Sara Aparicio.

Do you want a career at the cutting edge of new space technologies? Have you expertise in artificial intelligence, small-sats, high altitude pseudo-satellites, computer science, distributed ledgers, quantum computing or the new space economy? Do you want to use your knowledge to make new earth observation and space systems? Do you want to work in a fast-paced environment where teamwork makes your ideas a reality in 2 or 3 months? Then apply today for current open positions at ESA’s Φ-lab (Frascati, Italy).

Current open positions

• Φ-lab Research Fellow (8619). Closing date for application – 3 February 2019
• Digital Technologies Engineer (8632). Closing date for application – 31 January 2019

These posts are only open to nationals of ESA Member States or Cooperating States. The posts are in the Directorate of Earth Observation Programmes. ESA is an equal opportunity employer, committed to achieving diversity within the workforce and creating an inclusive working environment. Applications from women are encouraged, and priority is given to applicants from under-represented Member States. If you require support with your application due to a disability, please email contact.human.resources@esa.int. 

Φ-lab Background

The Φ-lab’s mission is to accelerate the future of Earth Observation (EO), by helping Europe’s earth observation and space researchers and companies adopt disruptive technologies and methods. Right now, we’re working with Artificial Ingelligence (AI) on data from the Copernicus programme, Earth Explorer missions and cubesat satellites, drone and hyperspectral payload data and VR. HAPs, quantum technologies and distributed ledgers are on our to-do list. Φ-lab is part of the ESA Earth Observation Programme’s Φ-Department developing future systems for earth observation. Φ-lab also hosts ESA’s InCubed programme, providing rapid funding of innovative public private partnerships to exploit new EO markets. Φ-lab also convenes experts from across the World to develop research agendas on the relevance for EO of emerging technology topics including AI, distributed ledgers and quantum computing.

Φ-lab tests concepts in 1-3 month Case Studies executed by a multidisciplinary research team that you’d be a part of. Find more about the on going Case Studies.

The Open SAR Toolkit – the API for SAR processing

There is a need for higher level processing software of SAR datasets for developers that don’t have deep remote sensing or signal processing backgrounds to still do interesting geospatial analytics. ESA Φ-lab is addressing this issue by working on such a tool with ESA’s SNAP Toolbox under the hood.

Initially, the Open SAR Toolkit (OST) was developed at the Food and Agriculture Organization of the United Nations (FAO) under the SEPAL project (https://github.com/openforis/sepal). It has now been completely re-factored and transferred into a simpler and less-dependency rich python3 version.

OST automates the production of small to large-scale Sentinel-1 time-series and timescan data and therefore lowers the entry barrier for pre-processing of Sentinel-1 data. Jupyter notebooks are developed on top of this core package and should help to get started.
The python package is available on our GitHub page (https://github.com/ESA-PhiLab/OpenSarToolkit) and can be easily installed using the python-3 pip package manger.

Post contributed by Andreas Vollrath.

How images of cats and dogs can help solving food security issues?

Current development in Artificial intelligence shows the strong ability of computers to learn information about our world. One of the most applicable methods artificial intelligence can offer is generalization of information based on some examples. These approaches applied to a certain problems resulted in a great success. For example, in computer vision domain, machines are now capable to classify any image into one of around a thousand of object categories with the accuracy comparable or even better than humans. The roots of the successful methods come from biomedical discoveries of how mammal brains analyse visual information.

This way is now being reflected in artificial intelligence by so-called deep learning. But our brain is capable to analyse not only images from our daily life environment, but also can recognize objects in satellite or drone images. So, why do not apply deep 

learning techniques for Earth observation images? The only problem is that to achieve a good results, deep learning methods need a huge number of learning examples, which is typically not assured in Earth observations.

This is why Φ-lab is conducting a research on how methods and knowledge developed for computer vision can be transferred to Earth observation images via a process called transfer learning. Our first success was to show that general information learned by deep neural networks pre-trained on images of objects like cats and dogs can be very useful in crop types mapping on drone images.

Partnerships

This achievement is now being explored with our partners, World Food Programme and UNICEF Innovation for improving agricultural monitoring which helps manage food supplies and children’s welfare in developing countries.

Rohingya children playing at a UNICEF child friendly space. Credit : Anna Dubuis /DFIDComputer vision & Food security issues

Post contributed by Artur Nowakowski.

SWARM analysis using a data-driven approach Machine Learning

During a three month research sprint, an analysis of SWARM data was carried out to support existing activities which aim to identify possible earthquake precursors. To maintain an unbiased approach, unsupervised machine learning techniques were carried out to identify any possible correlations with earthquakes. In the timespan of the research activity it is has not yet been possible to confirm or deny the utility of SWARM data as earthquake precursors. However, a framework is being built which facilitates researchers to carry out similar, and more in-depth, analysis of multimission datasets, including SWARM, for precursor assessment of earthquakes and other phenomena, such as volcanic activity and lightning, using machine learning.