The frequency and severity of fire weather have been projected to increase under climate change, particularly in high-latitude boreal forests. Lightning, a key ignition source globally, is also expected to become more frequent with climate change and could significantly increase burn area. Current research on lightning-ignited wildfire (LIWs) has a long history in boreal ecosystems but has typically focused on North America due to better data availability, while the lack of publicly available data for Eurasia has hindered our comprehensive understanding of important characteristics of LIWs, such as holdover time, lightning ignition efficiency, frequency, and spatial distribution in boreal forests. This study introduces the temporal minimum distance (TMin) method, a novel approach to matching lightning strikes with wildfires without requiring ignition location that outperformed current methodologies. As a result, we developed a comprehensive dataset of lightning-ignited wildfires across the entire boreal forest from 2012 to 2022, encompassing 6902 fires, 4201 in Eurasia and 2701 in North America, each over 200 ha in size. This dataset (https://doi.org/10.5281/zenodo.14940326, Engle et al., 2024) provides new opportunities to model the ignition and spread dynamics of boreal wildfires and offers deeper insights into lightning-driven fire activity globally.

The benefits of design decision support systems (DDSSs) in the architectural planning context have been proven in research and are increasingly used in practice. The sense and purpose are apparent. The weighing of the most diverse ideas and approaches are required for design problems that cannot be solved unambiguously and are characterized by complex, open issues of architectural design tasks, coupled with contradictory criteria. DDSSs support planners/decision-makers with objective information to support the decision-making process with well-founded data and statements. This is becoming increasingly necessary, especially given increasingly complex construction tasks, and thus the difficult-to-predict effects of decisions. Taking this maxim into account, however, also reveals challenges in the planning context, as well as the immense potential and fields of application. Building on these issues, this article presents a perspective for DDSSs. The paper discusses the current focus and advancements of such systems, highlighting the challenges such tools still face, and provides a vision of the perspective future of these systems from reactive systems to proactive assistance.

Leonhard Obermeyer Center Technical University of Munich Munich, Germany Established: 2014 Leadership: André Borrmann (Chair of Computational Modeling and Simulation) Frank Petzold (Chair of Architectural Informatics) Thomas H. Kolbe (Chair of Geoinformatics) Konrad Nübel (Chair of Construction Process Management) Uwe Stilla (Chair of Photogrammetry and Remote Sensing) Current Center Structure: Participating Chairs: 5 Research Staff: 70-80 Postdoctoral Researchers: approximately 10

The frequency and severity of fire weather have increased under climate change, particularly in high-latitude boreal forests. Lightning, a key ignition source globally, is also expected to become more frequent with climate change and could significantly increase burn area. Current research on lightning-ignited wildfires (LIW) has a long history in boreal ecosystems but has typically focused on North America due to better data availability, while the lack of publicly available data for Eurasia has hindered our comprehensive understanding of important characteristics of LIW, such as holdover time, lightning-ignition efficiency, frequency, and spatial distribution of lightning-ignited wildfires in boreal forests. This study introduces the Temporal Minimum Distance (TMin) method, a novel approach to matching lightning strikes with wildfires without requiring ignition location, that outperformed current methodologies. As a result, we developed a comprehensive dataset of lightning-ignited wildfires across the entire boreal forest from 2012 to 2022, encompassing 6,228 fires — 4,186 in Eurasia and 2,042 in North America — each over 200 hectares in size. This dataset provides new opportunities to model ignition and spread dynamics of boreal wildfires and offers deeper insights into lightning-driven fire activity globally.

The refurbishment and energy-efficient renovation of existing buildings, specifically those before 1945, with complex architectural building elements, require a level of building information that is often unavailable, incomplete or imprecise. As they represent a large percentage of current building stock (up to 25% of all buildings), it is crucial to address such an issue, as such buildings are ideal targets for renovation and energy retrofitting projects. This paper presents a conceptual pipeline, developed through the NERF2BIM research project, focusing on an Artificial Intelligence (AI)-supported holistic pipeline for the creation of as-is Building Information Modeling (BIM) models of existing buildings, expanding upon existing methodologies with the embedding of knowledge-driven semi-automatic detailing-on-demand task. The proposed pipeline integrates uncertainty-aware spatial capture, semantic interpretation and reconstruction, and knowledge-based reasoning within a BIM-oriented workflow. The paper provides an overview of current advancements in the respective aspects of the pipeline, highlighting current gaps. The proposed conceptual pipeline aims at addressing these issues through novel applications of AI and knowledge-driven solutions. Key contributions include: (1) a conceptual approach in addressing the imprecision of more sustainable data gathering approaches; (2) a context-aware BIM reconstruction process, providing multiple data output types; and (3) a formalization of architectural and construction knowledge and its utilization in a detailing-on-demand approach of the reconstructed BIM models. Through the integration of uncertainty-aware data gathering, context-aware reconstructions and domain expertise into existing reconstruction pipelines, the proposed pipeline bridges the data gap for existing buildings, enabling more efficient and knowledge-driven renovation processes.

Climate change is expected to change both fire susceptibility and lightning activity in global boreal forests. The mechanics governing lightning-ignited wildfire (LIW) ignition remain, however, unclear, making predictions at global boreal scale challenging. Here we examine short-term temporal impacts of weather on ignition probability using a novel dataset on LIWs in a quasi-experimental design of ignition and non-ignition lightning pairs. Specifically, our dataset is comprised of 5251 lightning pairs coupled with temperature, precipitation, relative humidity, lightning characteristics (multiplicity, polarity, peak current, and duration) and net primary productivity. We estimate the effect of these predictor variables on ignition probability across three separate models; the full boreal, open forests and closed forests. We find a clear relationship between ignition probability and temperature, relative humidity, total precipitation, and polarity. A one standard-deviation increase in temperature (∼3.3 °C) increased ignition probability by 44%–69%, while increased precipitation and relative humidity reduced ignition probability. Both temperature and precipitation were most predictive 3–8 d before lightning and the effect lasted up to 7–11 d after lightning. Positive polarity was also associated with increased ignition likelihood. Our results suggest that LIW occurrence within the global boreal is primarily driven by short-term fire weather rather than lightning characteristics alone. A projected increase in temperatures is expected to drive ignition probability in the boreal, though this may be partly offset by an increase in total precipitation.