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2025-04-15
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HafenCity Universität Hamburg (HCU): BlueGreenStreets 2.0
Im BlueGreenStreets Projekt wurden in der ersten Projektphase von 2019 – 2022 wichtige Grundlagen zu blau-grünen Infrastrukturen in Straßenräumen und Ansätze zur klimaangepassten, zukunftsfähigen Straßenraumgestaltung erarbeitet. Der Prozess beruhte auf zahlreichen Netzwerk- und Austauschveranstaltungen sowie der Anwendung innovativer Planungsmethoden in Reallaboren in Städten wie Berlin, Solingen und Hamburg. Ergebnis war die im März 2022 erschienene Toolbox BlueGreenStreets – Multifunktionale Straßenraumgestaltung urbaner Quartiere mit den zwei Toolbox-Bänden A – Praxisleitfaden und B – Steckbriefe (BGS-Toolbox 1.0).
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2025-02-18
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Godview AI
Godview is an AI-powered, interactive map that allows users to perform geographical searches using natural language queries.
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2025-02-12
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Maps Mania: 3D Print Your World
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2025-02-11
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BMI - Marktplatz der KI-Möglichkeiten
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2025-01-23
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Geospatial Community
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2025-01-15
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Index — The Philosophical Glossary of AI
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2025-01-13
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United for Smart Sustainable Cities (U4SSC) – United for Smart Sustainable Cities (U4SSC)
The United for Smart Sustainable Cities (U4SSC) initiative is a global UN collaboration, coordinated by ITU, UNEP and UNECE, and supported by a network of key partners, including UN-Habitat, CBD, ECLAC, FAO, UNDESA, UNDP, UNECA, UNESCO, UNEP, UNEP-FI, UNFCCC, UNIDO, UNOPS, UNU-EGOV, UN-Women, UNWTO, and WMO.
U4SSC serves as an international platform for exchanging knowledge and fostering partnerships to empower cities and communities in achieving the UN Sustainable Development Goals.
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2025-01-12
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Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?
Human-in-the-loop (HIL) systems have emerged as a promising approach for combining the strengths of data-driven machine learning models with the contextual understanding of human experts. However, a deeper look into several of these systems reveals that calling them HIL would be a misnomer, as they are quite the opposite, namely AI-in-the-loop (AI2L) systems, where the human is in control of the system, while the AI is there to support the human. We argue that existing evaluation methods often overemphasize the machine (learning) component's performance, neglecting the human expert's critical role. Consequently, we propose an AI2L perspective, which recognizes that the human expert is an active participant in the system, significantly influencing its overall performance. By adopting an AI2L approach, we can develop more comprehensive systems that faithfully model the intricate interplay between the human and machine components, leading to more effective and robust AI systems.
Sriraam Natarajan, Saurabh Mathur, Sahil Sidheekh, Wolfgang Stammer, Kristian Kersting
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2025-01-12
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Neues 3D-Modell von Stability AI soll schnell genug für Echtzeit-Generierung sein
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2025-01-11
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Google Launches Android XR, Its New AI-Powered Extended Reality Platform
