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2025-06-25
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Extract
Planning applications made to councils get compared against historic maps and other key policy documents that can be difficult to access. Important information is often stored on handwritten paper notes, microfiche or is locked away in PDF documents. Without it residents and developers struggle to understand what they can build or what home improvements are allowed in their area before making their applications. As a result, estimates suggest 250,000 hours are spent by planning officers each year manually checking documents and a third of applications submitted annually get rejected.
New digital software can help to streamline the planning application process. Government backed, open-source tools such as PlanX are being used live in councils to help the public to better understand what information to submit with their applications, reducing time to validate a pre-application from 6.5 days to 1.3 days in Doncaster Council, and reducing incoming calls to planning helplines by 60% in Camden, saving 21 hours per month. However, local planning authorities face a significant data barrier when trying to adopt this software because it requires data in modern formats to function effectively.
Armed with the right data and software, councils will serve the public more quickly, transparently and predictably to process more applications and permit more home improvements.Extract drastically reduces the effort required of local councils to become digitally mature by automating the conversion of PDF planning constraints into modern, usable data.
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2025-04-23
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Künstliche Intelligenz in Smart Cities: Chancen, Herausforderungen und Anwendungen | Teil 1 - YouTube
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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
