AI & Environment Resource HubLGND AI · Publication date unknown
[Geospatial Analysis] Specializing in geospatial AI for urban planning and environmental modeling.
LGND AI. Date unknown. Konstantin Klemmer. https://www.linkedin.com/in/konstantinklemmer/ (AI & Environment Resource Hub; record atlas-4cc6dd2f3e1e; collection snapshot 2026-09-15).
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Source sheet: People of Interest · Row 15 · Original ID: Not supplied.
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Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Biodiversity and ecosystems; Built environment and infrastructure
Deep learning combined with satellite remote sensing enhances biodiversity monitoring and conservation by leveraging big data, but challenges in capacity building, data access, environmental costs, and model interpretability must be addressed for broader adoption.
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Biodiversity and ecosystems; Built environment and infrastructure
[Geospatial Analysis] Applying AI to building stock data for urban sustainability and energy efficiency.
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Built environment and infrastructure
Philip Johnston is the co-founder and CEO of Starcloud, the company building data centers in space. In November 2025, Starcloud launched an Nvidia H100 GPU into orbit and trained the first large language model in space. They've since raised $200 million, hit a billion-dollar valuation just 17 months after YC demo day and filed with the FCC to deploy 88,000 more satellites. In this episode, Philip walks us through their wild origin story, the engineering challenges behind the Starcloud-1, why they booked a SpaceX launch before they even knew what they were building and how data centers in space make sense both economically and politically.
Shared topics: Cross-cutting sustainability; Research tools and geospatial methods; Biodiversity and ecosystems
Trust is a cornerstone and enabler of human civilization, determining the very nature of how people interact with each other. The swift integration of artificial intelligence (AI) into daily life poses grand societal challenges and necessitates a reevaluation of trust. Our bibliometric literature review calls for scientists and stakeholders to cross traditional academic boundaries to address emerging and evolving societal challenges arising from AI. We propose a transdisciplinary research framework to understand and bolster trust in AI and address grand challenges in domains as diverse and urgent as misinformation, discrimination, and warfare.