Integrating Concepts of Artificial Intelligence in the EO4GEO Body of Knowledge

Ponència del XXIV ISPRS Congress (2022 edition), 6–11 June 2022, Nice, FranceThe EO4GEO Body of Knowledge (BoK) forms a structure of concepts and relationships between them, describing the domain of Earth Observation and Geo-Information (EO/GI). Each concept carries a short description, a list of key literature references and a set of associated skills which are used for job profiling and curriculum building. As the EO/GI domain is evolving continuously, the BoK needs regular updates with new concepts embodying new trends, and deprecating concepts which are not relevant anymore. This paper presents the inclusion of BoK concepts related to Artificial Intelligence. This broad field of knowledge has links to several applications in EO/GI. Its connection to concepts, already existing in the BoK, needs special attention. To perform a clean and structural integration of the cross-cutting domain of AI, first a separate cluster of AI concepts was created, which was then merged with the existing BoK. The paper provides examples of this integration with specific concepts and examples of training resources in which AI-related concepts are used. Although the presented structure already provides a good starting point, the positioning of AI within the EO/GI-focussed BoK needs to be further enhanced with the help of expert calls as part of the BoK update cycle

Complementing the European earth observation and geographic information body of knowledge with a business‐oriented perspective

A body of knowledge (BoK) is an inventory of knowledge or concepts of a domain that serves as a reference vocabulary for various purposes, such as the development of curricula, the preparation of job descriptions, and the description of occupational profiles. To fulfill its purpose, a BoK needs to be up‐to‐date and ideally widely accepted by academia as well as the private and public sectors. This article presents the initiative taken in the Earth observation and geographic information (EO*GI) domain to provide a current, comprehensive education‐ and business‐oriented EO*GI BoK called EO4GEO BoK. In particular, an approach to strengthen the business‐oriented perspective in the EO4GEO BoK is presented. This approach is based on the analysis of professional tasks and the mapping of these tasks to concepts and skills contained in the BoK. A critical reflection of the proposed approach that is based on the experiences gained during a workshop complements this article

Updating and using the EO4GEO Body of Knowledge for (AI) concept annotation

The EO4GEO Body of Knowledge (BoK) serves as a vocabulary for the domain of geoinformation and earth observation, supporting the annotation of online resources. This paper presents how the BoK is designed, maintained and improved. We discuss how the BoK content can be extended, using the example of integrating artificial intelligence (AI) concepts and show how annotation is done by adding persistent concept identifiers in the metadata of training materials. This platform allows us to share online information with clarified semantics. A prolonged use necessitates the incentivisation of an active expert community and a further adoption of infrastructure standards

Polarimetric Parameters for Growing Stock Volume Estimation Using ALOS PALSAR L-Band Data over Siberian Forests

In order to assess the potentiality of ALOS L-band fully polarimetric radar data for forestry applications, we investigated a four-component decomposition method to characterize the polarization response of Siberian forest. The decomposition powers of surface scattering, double-bounce and volume scattering, derived with and without rotation of coherency matrix, were compared with Growing Stock Volume (GSV). To compensate for topographic effects an adaptive rotation of the coherency matrix was accomplished. After the rotation, the correlation between GSV and double-bounce increased significantly. Volume scattering remained same and the surface scattering power decreased slightly. The volume scattering power and double-bounce power increased as the GSV increased, whereas the surface scattering power decreased. In sparse forest, at unfrozen conditions the surface scattering was higher than volume scattering, while volume scattering was dominant in dense forest. The scenario was different at frozen conditions for dense forest where the surface scattering was higher than volume scattering. Moreover, a slight impact of tree species on polarimetric decomposition powers has been observed. Larch was differed from aspen, birch and pine by +2 dB surface scattering power and also by −1.5 dB and −1.2 dB volume scattering power and double-bounce scattering power respectively at unfrozen conditions

Estimation of Above-Ground Biomass over Boreal Forests on Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data

The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. In particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha−1). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha−1 to 4.7 t ha−1; and a RMSEcor of 30.2 t ha−1 was calculated

Impact of COVID-19 on eLearning in the Earth Observation and Geomatics Sector at University Level

The COVID-19 pandemic has a high impact on education at many different levels. In this study, the focus is set on the impact of digital teaching and learning at universities in the field of Earth observation during the COVID-19 pandemic situation. In particular, the use of different digital elements and interaction forms for specific course types is investigated, and their acceptance by both lecturers and students is evaluated. Based on two distinct student and lecturer surveys, the use of specific digital elements and interaction forms is suggested for the different course types, e.g., academic courses could be either performed asynchronously using screencast or synchronously using web meetings, whereas practical tutorials should be performed synchronously with active participation of the students facilitated via web meeting, in order to better assess the student’s progress and difficulties. Additionally, we discuss how further digital elements, such as quizzes, live pools, and chat functions, could be integrated in future hybrid educational designs, mixing face-to-face and online education in order to foster interaction and enhance the educational experience

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.Peer Reviewe