ALBERT

Description: Technology transfer (TT) between academia and industry is an important source of innovation and economic development and is becoming increasingly relevant across a diversity of research fields. Successful TT depends on effective communication between academic and external partners. This is best facilitated by an academic intermediary who can provide meaningful interactions. An academic intermediary can ensure expertise and knowledge are communicated using a common language and that goals and expectations are clear between partners. In the field of remote sensing where complex technologies, datasets and analyses are required to develop user friendly products, intermediaries play a particularly essential role. The rapid expansion of the remote sensing sector over the last decade, including sensor technologies and the volume of freely available data, has created a bottleneck between available data and technology and ready to use products. In an effort to address this bottleneck and establish a long-term innovation platform and thematic TT infrastructure, FERN.Lab, Remote Sensing for Sustainable Use of Resources Helmholtz Innovation Lab was founded at the Geodesy Department of the German Centre for Geosciences (GFZ) in January 2020. FERN.Lab is funded by the Helmholtz Association, the largest scientific organization in Germany. The goal of FERN.Lab is to facilitate TT and deliver remote sensing products to commercial and non-commercial partners by acting as an expert intermediary platform. FERN.Lab will improve the TT activities of GFZ from two distinct approaches. The first is a “pull” approach where tailor-made technologies are developed for and funded by external third parties. The second is a “push” approach where existing departmental technologies with high market potential are promoted. The pull and push of technologies to external partners is accomplished by a set of competencies and services delivered by the core FERN.Lab team. Competencies include business development, scientific development, software development, and public relations which directly address institutional, financial and skills gap that can cause the TT process to fail. By implementing a robust TT framework for remote sensing products, the impact of research has the potential to be much broader and farther reaching. Additionally, these efforts can improve the acceptance of remote sensing outside of academia improving and modernizing methods used in diverse sectors which in turn can benefit not only individual partners but also politics, society, and the environment. As application-oriented innovation platform, with a close interaction with partners and customers, FERN.Lab will be a crucial part of modern TT at GFZ.

Description: The Minimal Sampling Classifier – MiSa.C - is an innovative classification webservice for remote sensing image raster data that allows a classification of complex surfaces over space and time by considering only a minimal amount of training samples as reference input data. Based on only one reference sample per class, the tool incorporates images statistics and machine learning to automatically create an enlarged set of unbiased and comprehensive training data, which is used as foundation for the creation of a large number of models representing the classification results. Users with expert knowledge for at least a small, representative region of the entire classification area can then evaluate the model outputs in a step-wise approach, that provides the flexibility to fine-tune each classification decision by thresholding the number of models used as basis for the final classification. A big surplus of MiSa.C is the ability to process multiple source imagery together that are provided in one 4D data-cube (areal, spectral, time domain), e.g. a combination of optical imagery, radar imagery and a digital elevation model or else. Originating from the GFZ Potsdam German Research Centre for Geosciences in-house software development called “Habitat Sampler”, MiSa.C is an example for successful technology transfer from an innovative open source code to a user-friendly Software-as-a-Service (SaaS) webservice. The development was undertaken by the Helmholtz Innovation Lab FERN.Lab, which is part of the research centre. The lab planned and implemented the webservice in close cooperation with the lead scientist and developed an intuitive graphical user interface suitable for all kind of users coming from both the public and the private sector. MiSa.C is especially designed for close monitoring of locally delimited regions that are dominated by mixed-pixel areas with typical spectral patterns and phenological changes over the time such as habitats and other ecosystem areas. We demonstrate applications of the Non-Governmental Organisation Heinz-Sielmann-Stiftung where MiSa.C is used to classify different habitat types in former military areas, which are not allowed to access nowadays due to ammunition load. Results of the change of habitat types over the time allow caretakers and decision makes to fulfil their report duties and to carefully plan their future restoration measurements. Further examples show forest applications, such as tree species detection and classifications of crops within the agriculture domain. In an outlook, the authors also encourage users to use MiSa.C as an automatic training data generator that provides the necessary source data for other supervised, AI-based classifiers.

Description: Knowledge and technology transfer (hereafter referred to as KTT) between academia and society has long been recognized as a key driver of innovation and economic development. Knowledge transfer (KT) is defined by Bloedon and Stokes, (1994) “as the process by which knowledge concerning the making or doing of useful things contained within one organized setting is brought into use within another organizational context”. Similarly, technology transfer (TT) can be defined as the movement of a specific technology from one place to another. Technology transfer (TT) between academia and industry is an important source of innovation and economic development. Successful TT depends strongly on effective communication between academic and external partners. This is best facilitated by an academic intermediary who can provide meaningful interactions. An academic intermediary can ensure expertise and knowledge are communicated using a common language and that goals and expectations are clear between partners. In the field of remote sensing intermediaries play a particularly essential role. The rapid expansion of the remote sensing sector over the last decade, including sensor technologies and the volume of freely available data has created a bottleneck between available data and technology and ready to use products outside of academia. Regional and municipal government agencies, small-medium enterprises (SMEs), and Non-Governmental Organizations (NGOs) who would greatly benefit from remote sensing data often lack expertise and capacity to produce or access high quality products and technologies. Additionally, large private sector companies with expertise in internal Research and Development (R&D) departments who operate on profit-driven strategies may limit investment in new untested RS solutions because of limited market size or monetarization risks. Academic institutions are well positioned to act as intermediaries and address this bottleneck through the implementation of robust KTT strategies to help meet regional, national, and international demand for high quality RS data and technology. Several initiatives have been made by Space Agencies, academic institutions or private companies covering broad range of KTT work but upon closer examination. All these initiatives focus on profit-based operation predominantly by licensing developments and products or the creation of spin-offs. The following aspects are not considered: 1. Missing benefit for scientists. Most current KTT frameworks are viewed as mutually exclusive or as direct competition for resources and reputation with scientific work. This results in lost opportunities for further operational development of innovative ideas being developed in research settings. 2. Undervaluation of the social impact of KTT. KTT is often seen as a profit-driven initiative only. In our opinion More weight and value should be put on the social, political, and environmental impact of KTT activities to broaden the reach and participation. 3. Combining open science with commercial use. KTT can and should focus on the simultaneous development of open-source community and commercial versions with advanced functionalities to maintain the principles of open science which are central to current good scientific practice. 4. KTT should not only be viewed as an exit strategy for scientists to leave academia. KTT should be embedded in institutional frameworks to encourage and inspire scientific developments from scientists pursuing academic careers. To address this bottleneck and establish a long-term innovation platform and thematic TT infrastructure, FERN.Lab, Remote Sensing for Sustainable Use of Resources Helmholtz Innovation Lab was founded at the Geodesy Department of the German Centre for Geosciences (GFZ) in January 2020. FERN.Lab is funded by the Helmholtz Association, the largest scientific organization in Germany. The goal of FERN.Lab is to facilitate TT and deliver remote sensing products to commercial and non-commercial partners by acting as an expert intermediary platform. We will present two distinct approaches to improve this bottleneck from science market and society 1. The first is a “pull” approach to develop tailor-made technologies for and funded by external third parties. 2. The second is a “push” approach to promote existing departmental technologies with high market potential. The pull and push of technologies to external partners is accomplished by a combination of competencies and services. This includes business development, scientific development, software development, and public relations. All of them directly address institutional, financial and skills gap that can cause the TT process to fail. By implementing a robust TT framework for remote sensing products, the impact of research has the potential to be much broader and farther reaching. Additionally, these efforts can improve the acceptance of remote sensing outside of academia improving and modernizing methods used in diverse sectors which in turn can benefit not only individual partners but also politics, society, and the environment.