In the EOCafe, “where the EO Community meet”, our subject for discussion had a more applications driven focus. This time it was dedicated to the use of EO information for the raw materials sector. “Raw materials” includes mining but also other means of extraction and even recycling as a means for production. Although, for the EO community, it could be obvious that EO data/information is useful for mining companies in several of its stages of operations, (from the exploration phase, environmental assessment & permits, to design, operations, mine closure and aftercare), for the mining sector there is still a long way to go for this relationship to succeed.
Margreet Van Marle, Consultant of Wildfires and Climate Resilience at Deltares, introduced the ESA funded project EO4RM project, which is a “best practice” project building upon the model which was successfully developed with the Oil and Gas sector some years ago. The best practice process identifies the demand through a list of challenges which the sector face. It then looks at what EO services can help overcome those challenges and subsequently the gaps and the barriers to uptake in the sector.
To put this into perspective, Reinier OOST, Product Lead at Sensar and Brendan Morris, Managing Director of LTMS (Lisheen Technical & Mining Services) gave examples as showcases. These showcased 15 EO products and their potential for the mining sector, including monitoring of tailings, subsidence monitoring of open pits and detection of environmental damage.
Even if EO technologies are not unknown to the mining industry, and especially the larger companies, there is still much room to increase their use, mainly through raising awareness of EO in the medium and small companies who have no knowledge or access to EO data according to Reinier. Once more, the main challenge EO technologies faces is awareness of EO and its capabilities along with the need of skills and expertise to be built within the mining companies. However, they are not the only ones. To go deeper, Margreet explained that even if an EO company with expertise provides high level products but does not understand the demands of the mining company, then the product will not fit the needs and meet the mining company needs. Vice versa, if the mining side lacks skills and knowledge, it will be challenging to make accurate requests, as its understanding in the EO technology such as processing, and interpretation of imagery could represent a barrier to procurement. According to Reiner, the image of EO in the mining sector is still science and research-based. Whilst this might have been true 10 or more years ago, the EO sector is now much more business orientated.
Reiner mentioned regulations as a driver. If norms were put in place under a regulatory framework at an EU level, this could enforce the predictability on mining management with the use of new technologies, amongst them EO, that is not there at the moment. In addition, the audience raised the topic of safety as an important topic in the mining industry, which should be put more attention worldwide as a legal obligation for the industry to invest in technologies to guarantee safety monitoring approaches.
Although international policy actions on safety are in force, these are under the umbrella of environmental protection, such as UNEP setting global environmental agenda including sustainable mining, the use of technologies for personal safety is still weak. For the record, the EU counts with a Directive[1] establishing minimum requirements for improving the safety and health protection of workers, however, it dates from 1992, which consequently EO technologies are not considered as a resource to enforce this legislation. It could be time to consider the use of EO in another piece of EU legislation to improve its enforcement.
Validation emerged as another barrier for the sector i.e. how can the products and services be shown to meet acceptable standards. Data is needed on a global basis to raise confidence in the services which are offered. Both some form of certification of the processes as well as validation of the satellite data will be necessary.
In conclusion, the main question is how these two sectors can develop a more fruitful relationship? The aforementioned oil and gas (O&G) sector created an informal advisory group with the EO sector called OGEO and which later became a sub-committee under the O&G umbrella association the IOGP. Can similar structures be envisaged under the mining and raw materials sector? Both Reiner and Brendan thought that this would be possible under either a European or International umbrella – which seems like a good recommendation, endorsed by the EOcafe participants, for the next steps.
Geoff Sawyer & Sandra Alvarado
For further reading:
Mineral Exploration from Space
https://www.esri.com/about/newsroom/arcwatch/mineral-exploration-in-the-hyperspectral-zone/
[1]See Directive 92/104/EEC https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31992L0104:EN:HTML
Approximately 180 attendees gathered on the EARSC second EOcafe of 2021 to learn more on how Artificial Intelligence (AI) can help improve and interact with Earth Observation (EO) services. Both of our invited guests represent the community of AI developers and the community of EO service companies to give a wider perspective on the topic.
Nicolas Longepe - EO data Scientist in the European Space Agency’s Φ-lab (Phi-lab)- explained how the ESA Φ-lab provides the platform for companies and individuals to become more familiar with EO services and especially to bring new technology to bear upon the sector. The recent focus has been on AI and new missions even sometimes combined. This has led to establishing the AI4EO project initiative. Nicolas also cited the In-cubed programme as of possible interest for those seeking to develop new activities/products together with ESA’s help.
Annekatrien Debien -Head of the Brussels office of SpaceTec Partners and lead of the AI4EO project- considers that bridging the two communities is not an easy task. In fact, the AI community is very diverse since AI is a transversal tool. Why? Due to the mix of industrial and research experts in the AI community. It is clear that satellite images are not just a picture from which you extract only one parameter, but a complex dataset that aims to tackle complex challenges that can actually profit from AI community knowledge.
All of the datasets taken from the satellites could get some insights on the parameters using AI. Thanks to AI, in general we are talking about prediction, detection, classification, big data analytics and super resolution emulation. Therefore, there is knowledge in both communities that can be complementary. However, one of the main challenges is to bridge the gap between the AI and the EO by fostering its use for example in the scientific community.
Dealing with EO problems, AI can be considered as a transversal and beneficial tool for EO contributing with all its techniques. For example, CNN (Computer Neural Network) which is adopted for segmentation (SAR) or GAN (Generating Adversal Network) for synthesizing images, mostly based on computer vision technics. Nevertheless, although a lot of different functions are already used there is still the need to explore them as only a small part of AI techniques are used. Linking the communities, finding partners and expertise remains the main challenge. To address this, Annakatrien currently the first phase is to build awareness in both communities to introduce both technologies. In addition, she explained the AI4EO challenge on air quality monitoring using Sentinel data promoted by ECMWF[1] is currently open and encourages the industry to submit its applications.[2]
Another issue perceived by the audience is the existing limitations on the robustness of the techniques and how both communities can benefit from their experiences to overcome such limitations. In addition, big data management should also need to be taken into consideration and the cloud solution, linked to the need to sustainability. This is illustrated by the use of Google and Amazon platforms and the place that the DIAS can play to address this demand. The audience also pointed out a limitation which is the lack of governmental research and data to reach a better understanding on how to get open tools, such as machine learning to explore the access to data, while academic and industry work are available.
Prompted by many questions from those in the EOcafe, Nicolas recognized that there should be more training efforts to provide access to data and other capacity building activities to raise awareness on AI and other technologies which is also a subject of interest for the Φ-lab. On the same line, the attendees also expressed their wish to know more on AI data trainings and object identification in AI and EO resolution gap. Trust on algorithms was a possible answer by Nicolas to start to resolve this applicability between these technologies.
Despite these concerns and issues raised, it was pointed out that there are already applications using AI in the EO community using models or algorithms with several levels of maturity that could be taken into consideration for the future of EO and AI possible synergy.
Finally, several attendees took the opportunity to promote resources and future events:
- Workshop under the EO4GEO project on 2nd March[3] (Daniela Iasillo).
- AI toolbox available called AiTLAS[4] (Dragi Kocev – Bias Variance Labs)
- A session on AI planned at IGARSS 2021 (Vasilis Kalogirou – EU SatCen).
- Machine Learning Datasets Library[5]
To assist AI and EO communities on fostering partnerships please write to AI4EO via the website[6]. Any EARSC member wishing to get involved can also contact the secretariat who will be pleased to help where possible.
Sandra Cabrera Alvarado, Aaron Scorsa, Geoff Sawyer
[1] European Centre for Medium-Range Weather Forecasts, [2] For applications submission visit: www.ai4eu.eu, [3] See www.eo4geo.eu ,
[4] See https://github.com/biasvariancelabs/aitlas/, [5] See https://www.paperswithcode.com/datasets, [6] For applications submission visit: www.ai4eu.eu