Education for sustainable development (ESD) requires new and updated approaches, including the use of information and communication technologies like digital geomedia. However, despite their numerous possibilities, digital geomedia appear to be under-utilized in ESD, meaning that their full potential remains unused. This leads to unresolved questions: How are digital geomedia currently used in ESD, and what form do they take? What are the obstacles and possibilities for integrating digital geomedia in ESD? How can we address them? To answer these questions, the ESDplus project conducted an online questionnaire among organizations that carry out ESD (protected areas, museums, nature conservation associations and zoological gardens). The results show that one-third of the respondents have never used digital geomedia, and only 4% apply digital geomedia in their ESD activities. Even though almost two-thirds see great potential in the use of digital geomedia, a majority still prefer to use traditional analogue methods in ESD. The following issues are key to improving the use of digital geomedia: providing an overview of digital geomedia; their integration in ESD activities, and the provision of suitable training materials.

The Fridays For Future movement brought the issue of climate crisis an enormous boost in public awareness and generated great interest in climate-related topics among students. The particular way in which issues of climate protection or climate adaptation are negotiated by social media communities ensures their appeal. Two questions arise from this: Which digital practices do activists of Fridays For Future use to disseminate knowledge on climate and climate change? How do these practices affect informal learning processes in the knowledge domain of climate and climate change? Results obtained from a qualitative content analysis of posts and documents created by members of digital communities of the Fridays For Future movement and interviews with activists show that processes of identity formation take place in the communities. If these knowledge practices are taken up and reflected upon in the classroom, they can be transferred to formal educational processes, in terms also of transformative learning.

Natural language processing systems like ChatGPT have recently attracted enormous attention in the field of higher education. We aim to contribute to this discussion by scrutinizing the suitability of current testing methods and potentially necessary shifts in learning objectives in GIScience. This paper presents an anecdotal approach to the impact of ChatGPT on teaching and learning based on a real-world use case. It focuses on the results of a fictional student who used ChatGPT for the completion of applicationdevelopment assignments, including coding. The solutions were submitted to the instructor, who assessed the results in a single-blind experiment. The instructor's feedback and grading as well as the AI-plagiarism results were part of our evaluation of the testing methods applied. This triggered a discussion on the adequacy of current learning objectives in the development of GIS applications and the integration of AI into the learning process.

In the semi-arid climate of Kyrgyzstan, the rivers have a particular importance in providing water for irrigation in agriculture, and for the production of hydropower. Kyrgyzstan is in general rich in water resources. Nevertheless, there are conflicts surrounding the operation of Toktogul, one of the most important reservoirs in the region. Exploiting the hydropower potential of the currently free-flowing upper Naryn River is therefore an ongoing planning issue. Despite the obvious advantages, the planned construction of Kambarata-1 hydropower station would cause a significant alteration of the natural conditions of the Naryn. However, there are no recent studies on the ecological impact of this hydropower project. The present study contributes to filling this gap, delivering up-to-date information on the potential impact of the reservoir construction on the natural ecosystems in the area. The results show that, due to the geomorphological characteristics of the reservoir site, it is mostly steppe ecosystems that will be affected by the reservoir construction, and only a relatively small area of floodplain forest will be destroyed. In conclusion, the ecological impacts of Kambarata-1 HPS are minimal due to the specific characteristics of the location.

More than half of the U.S. dams are privately owned and experienced the overgrowth of trees. There is a need to improve dam inspection and maintenance in a timely manner. Small Unmanned Aircraft Systems (sUAS) have been increasingly utilized for near-surface landscape mapping and reconnaissance. This study tests an sUAS protocol of closed-canopy tree survey on earthen dams. A DJI Matrice 100 flight was launched on September 22, 2020. The orthoimage and 3D point cloud are extracted, and the canopy height model is built. Treetops and crowns are delineated using an integrated watershed segmentation and image segmentation procedure. The results include a tree survey inventory that contains the locations, tree heights and crowns of 284 trees growing on the downslope of the dam. Given the flight flexibility and fine 3D details acquired from inexpensive drones, sUAS has a high potential for assessing tree overgrowth toward remediation solutions of earthen dams.

Forest Flux https://www.forestflux.eu/ will renew forestry value-added services in Earth Observation (EO) by creating and piloting cloud-based services for committed users on forest carbon assimilation and structural variable prediction. Forest Flux exploits the explosive increase of high-resolution EO data from the Copernicus program and developments of cloud computing technology. It implements a world-first service platform for high-resolution maps of traditional forestry variables together with forest carbon fluxes. Forest Flux will allow the users to improve the profitability of forest management while taking care of ecological sustainability. The Forest Flux services are implemented on the Forestry Thematic Exploitation cloud platform https://f-tep.com/. In 2020, nearly 700 thematic maps on forest stand and carbon flux variables were delivered to nine specific users in a form that was applicable to their operational forest management systems. The last project year 2021 focuses on map product refinement and improving user services, which eventually lead to operational service concepts. Forest Flux is an Innovation Action project of the European Union, Grant Agreement No. 821860.

The development of big remote sensing data related technologies and applications poses a big challenge that massive computing capability is needed to support big data processing. In order to solve this challenge, this paper proposes an architecture of heterogeneous platform of high performance computing, which employs the computer hardware resources to improve the efficiency of big remote sensing data processing by optimizing scheduling strategies and designing high-performance algorithms. Furthermore, the proposed platform can dynamically incorporated with a workflow engine regarding big remote sensing data processing. These algorithms are modular to meet the flexible combination of different processes.

This paper demonstrates the use of remote sensing in planning urban green spaces (UGSs). UGSs emerged as a popular solution to combat the effects of Urban Heat Island, especially in tropical cities. UGS projects often need to identify priority implementation areas due to limited funding for UGSs. This study includes two Asian cities, namely Phnom Penh (Cambodia) and Chittagong (Bangladesh). It is not comparative, but it has identified priority administrative areas for future UGSs in both cities. We used Landsat 8 data and the remote sensing technique Land Surface Temperature (LST) analysis using radiance, temperature brightness and emissivity. LST data were then intersected with the administrative boundaries of the study areas. The identification of priority administrative areas for UGS considered both the area coverage and the percentage of coverage in terms of maximum LST within the administrative units’ boundaries. The result found 8 and 10 administrative units to be hotspots for UGSs, for Phnom Penh and Chittagong respectively. The proposed method will be useful to both government and non-government organizations alike, especially in tropical countries.Smart cities, despite their supposed benefits, also pose challenges to inhabitants. The promised improvements of quality of life, sustainability and efficiency are accompanied by a lack of data security and privacy, and a loss of spontaneous and chaotic but enjoyable urban life.

Sustainable Development Goal (SDG) target 7.2 requests a substantial increase in the share of renewable energy in the global energy mix by 2030. Renewable energy production in all sectors has to be evaluated for its contribution to reach this target. Biomass for energy production has gained a bad reputation over the past years due to the “food versus fuel” debate or reported unsustainable practices. The BIOPLAT-EU project is employing geoinformation technologies combined with sustainability and economic expertise to more accurately evaluate the sustainability of bioenergy value chains. The project has three main parts: first, the generation of a pan-European map of marginal, underutilized, and contaminated (MUC) lands potentially usable for bioenergy production. This is realized by employing remote sensing time series, existing Copernicus, and other spatial data sets. Second, the generation of a web-based geographical information system (GIS) connecting the MUC lands with other important information sources necessary to assess sustainability. Thisrd, the sustainability assessment includes not only typical social and environmental sustainability indicators like soil, water, or greenhouse gas emissions, but also economic sustainability indicators like employment. Current financial barriers are addressed by integrating innovative financing solutions considering SDG target 12.A.

In this paper, we show a framework for partial bot rejection based on spatially supervised text mining from social media messages. We show qualitative results towards the reduction of known bots and give hints on how this cleaning technique can help us in filling gaps of current signals related to human life on Earth based on social media. The bot rejection framework is based on using a spatial signal for supervising a machine learning model with extreme label noise still being able to reject some of the unwanted components of the social media stream. Furthermore, we comment that such models show significant biases and can, therefore, not be used responsibly without bias analysis and mitigation per application.