AI & Environment Resource HubTU Berlin · Publication date unknown
[Geospatial Analysis] Applying AI to building stock data for urban sustainability and energy efficiency.
TU Berlin. Date unknown. Nikola Milojević-Dupont. https://www.linkedin.com/in/nikola-milojevi%C4%87-dupont-9901a4128/ (AI & Environment Resource Hub; record atlas-e3b29382f821; collection snapshot 2026-09-15).
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Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
AI and ML optimize optoelectronic systems for sustainability by enhancing energy efficiency, renewable energy harvesting, environmental monitoring, and smart cities while also advancing ocean optics and photonics for marine ecosystem monitoring and climate change mitigation.
Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
To meet the growing demand for power of AI, we have to enable the entire ecosystem, from chip to grid. This lightning talk brings together how NVIDIA is working with its partners from power generation and transmission to distribution to build flexible and reliable infrastructure. The session focuses on AI for meeting energy demand, using digital technologies inside data centers to manage peak loads, and applying accelerated computing to simulate the grid and build physical infrastructure.
Shared topics: Cross-cutting sustainability; Energy and electricity; Biodiversity and ecosystems; Built environment and infrastructure
Nature Forward invites you to learn about the major impacts of data center development and how you can advocate for more responsible and sustainable data center development in your community. The course includes seven virtual sessions led by leading researchers, scientists, and advocates involved in the movement for more sustainable data center development.
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.