|Paper title||Global Earth Monitor|
|Form of presentation||Poster|
Global Earth Monitor (GEM; funded by H2020) takes advantage of the large volumes of available EO and non-EO data to establish economically viable continuous monitoring of the Earth, driven by the dynamic transition between "strip mode" and "spot mode" monitoring. GEM’s approach is based on the drill down mechanism: fast (and cheap) global processing at low spatial resolution, finding the areas of interest (AOI) where it triggers spot monitoring with (appropriately) high spatial resolution data and more elaborate machine learning (ML) models. Such processes can run continuously on a monthly, weekly, or even daily basis provided they work in a sustainable way - adding more value than their cost - at least on a continental if not global scale, able to automatically improve accuracy and detect changes as they occur.
The GEM consortium is formed by Sinergise (the coordinator), one of the key enablers of the uptake of Copernicus data through its well-known Sentinel Hub services; the European Union Satellite Centre (SatCen), one of the main European Institution of the Space and Security domain; meteoblue, a first-class weather services provider offering weather predictions at global scale on scales not familiar before from other weather services; TomTom, a well-recognized industry leader in location technologies; the Technische Universität Munchen (TUM), a research institution playing a vital role in Europe’s technological leadership. Each partner is in charge of implementing one use case, while TUM has a transversal role to support the development of AI tools relevant for the different use cases activity.
Long temporal series over very small areas (e.g., agricultural fields) and large scale (global) mosaics over shorter time stacks are two orthogonal use-cases when striving for efficient EO data retrieval. The concept of adjustable Data Cubes (aDC) addresses the two: a service capable of preparing the data in a way that the user needs it in her downstream pipelines and applications in a scalable and cost-effective way. At GEM, Sentinel Hub services are trying to address precisely that: cover both (corner) cases of data retrieval from the perspective of scalable and cost-optimised infrastructure. When coupled with the available data collections, the advantage of adjustable data cubes and analysis ready data (ARD) processing chains is enormous. Users can delegate the heavy machinery and processing of complex calculations (see e.g., custom scripts repository ) of large scale (mosaic) processing to the BatchAPI and feed the results into their own pipelines. The StatisticalAPI, and the upcoming Batch Statistical API, are preparing the aDC ARD for the other extreme: fast retrieval of long time series statistical variables (mean, min, max, std, percentiles, histograms) over AOIs, allowing for e.g., development of vegetation index time series of an agriculture parcel.
The EO industry seems to be evolving into two distinct branches: “we provide/sell data and data products” or “we provide a platform where users can build their own bespoke products”. GEM project tries to balance the two, leveraging the access to the data through the services, and providing users with open-source ways to build their own products using open-source eo-learn .
The development of scalable and cost-effective solutions is being tested on several use-cases. Built-up area use case identifies new built-up areas using the drill-down method. It exploits the Global Mosaic ARD cube of Sentinel-2 data at 120 m resolution as a starting point and, after fast detection of built-up areas at that resolution, runs the process at 10 m resolution to classify artificial surfaces and to detect changes. At that point, very high-resolution imagery is used to detect buildings. A Conflict Pre-Warning (CPW) Map use-case will provide a new security product to support decision-making. It analyses correlations between global climate changes and environmental issues with human activity behaviours, in support to guaranteeing the security of citizens. Automatic crop identification uses a combination of EO and weather data to enable automatic identification of crop growth stage. The use case supports operational decisions when managing crops and the quantitative monitoring of actual vs. planned or reported land use (production forecast). Map-Making Support use-case will integrate Land Cover services to perform a fully automated and repeatable global land cover mapping for small-mid scale and optimised land cover map at large scale (change detection functionality).
Within all use-cases we make use of the big data functionalities of Sentinel Hub for two purposes. Firstly, to showcase the capabilities of the increased performance, cost-effectiveness and scalability of services and framework for continuous monitoring using drill-down mechanism. And secondly, to demonstrate the adoption of use-case results for the decision making within industrial (e.g., map making), societal (e.g., conflict pre-warning maps) and other domains (e.g., crop identification for common agricultural policy).
The this talk we will provide an overview of the tools and use-cases developed within GEM, showcasing the bigdata capabilities of the services and their integration with eo-learn.