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2026-02-11
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New Report Indicates a Growing Public Resistance to Data Centers – Mother Jones
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2026-02-11
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Metadata Quality EU Dashboard
The Metadata Quality Assurance is intended to help data providers and data portals to check their metadata against various indicators. For information on which metrics we use for indicator measurements, please have a look at our methodology page.
This page provides a general overview of all metadata available to data.europa.eu. For a detailed view of each individual catalogue, please use the Catalogues page and select the desired catalogue.
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2026-02-09
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GitHub - UDST/urbansim: Platform for building statistical models of cities and regions
Platform for building statistical models of cities and regions - UDST/urbansim
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2026-02-09
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Data Center Infrastructure in the United States, 2025 (Map) - National Laboratory of the Rockies
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2026-02-09
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13pt How to Give a Talk
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2026-02-02
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Project Genie
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2026-02-02
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AI Goes Synthetic to Get Real
Synthetic data can take the place of data that does not exist in the physical world to train large language models.
Samuel Greenland
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2026-01-30
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Exascale-Computing
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2026-01-30
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Building Community-First AI Infrastructure
Microsoft is launching a new initiative to build what we call Community-First AI Infrastructure—a commitment to do this work differently than some others and to do it responsibly. This commits us to the concrete steps needed to be a good neighbor in the communities where we build, own, and operate our datacenters. It reflects our sense of civic responsibility as well as a broad and long-term view of what it will take to run a successful AI infrastructure business. In short, we will set a high bar.
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2026-01-23
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Doku der Föderalen IT-Kooperation (FITKO)
Diese Online-Dokumentation wird entwickelt und betrieben von der Föderalen IT-Kooperation (FITKO).
Sie vermittelt einen Überblick über verschiedene Themen im Kontext der Föderalen IT. Sie unterstützt bei der Entwicklung von IT-Systemen von Bund, Ländern und Kommunen.
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2026-01-13
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Marktplatz der KI-Möglichkeiten
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2026-01-12
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Simulating urban greening before anyone picks up a shovel
As cities get hotter, planners need good temperature data to figure out where to focus their cooling efforts. But there’s a bit of a problem. Traditional machine learning models need tons of local data to make accurate predictions, and that data often doesn’t exist for the cities that need it most. A new study from IBM Research tests whether geospatial foundation models (GFMs) can fill this gap.
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2026-01-12
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Google’s new ‘Earth AI’ framework
Google Research has released a working paper introducing ‘Earth AI’, a family of geospatial AI models paired with an agentic reasoning system. The main idea is to move beyond the traditional approach of using single, siloed models for specific tasks (like analysing satellite imagery or population data) and instead combine multiple foundation models that can ‘talk’ to each other.
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2026-01-08
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CityDreamer4D | Infinite Script
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2026-01-08
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GaussianCity | Infinite Script
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2026-01-07
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AI and the Next Economy – O’Reilly
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2025-12-17
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Die interaktive Datenraumlandkarte
Datentreuhänder, Datenräume, Forschungsdatenzentren und die Nationale Forschungsdateninfrastruktur (NFDI) nehmen Schlüsselrollen für den vertrauensvollen Umgang mit Daten ein.
Wir bieten einen Überblick über vielfältige Projekte und Initiativen, die auf das innovative Teilen von Daten zielen. Die Karte bietet die Möglichkeit, die Projekte nach Art der Aktivität, Förderung und Domäne zu filtern. Sie dient der Orientierung und bietet weiterführende Informationen.
Diese Karte wird von DaTNet, dem Datentreuhandkompetenznetzwerk, betreut und kontinuierlich weiterentwickelt. Sollten Sie Rückfragen, Korrekturwünsche oder Anregungen haben, wenden Sie sich gerne direkt an DaTNet.
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2025-12-15
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open bydata - Das Open-Data-Portal für Bayern
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2025-12-07
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Senfcall – Gib deinen Senf dazu!
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2025-11-24
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Vector Maps & 3D Terrain Models — Architecture & Planning | TopoExport
Download accurate 2D maps & 3D models: buildings, parcels, roads, contour lines, terrain. DXF, IFC, OBJ, STL formats.
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2025-11-15
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Verhaltensscanner im Mannheim: Hier wird die Überwachung getestet, die so viele Städte wollen
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2025-11-15
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Organic Maps - Personal Data
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2025-11-15
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The Glasses Free 3D Map
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2025-11-10
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Rising Tides: Broadening Public Participation in Climate Action through Mixed Reality Visualization
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2025-09-29
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mobi.mapr
Wie gut ist unsere alltägliche Mobilität wirklich?
Das Projekt mobi.mapr des Baden-Württemberg Instituts für Nachhaltige Mobilität (BWIM) will genau das herausfinden – und sichtbar machen. Mit Hilfe eines Scoringsystems wird bewertet, wie einfach und effizient sich Menschen in ihrer Umgebung fortbewegen können. Ziel ist es, Mobilität messbar und vergleichbar zu machen – für Einzelpersonen ebenso wie für typische Nutzergruppen. Damit liefert mobi.mapr einen spannenden Beitrag zur Frage, wie zukunftsfähige und lebenswerte Städte gestaltet werden können.
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2025-09-23
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Maps Mania: Making Maps Easy with Ultra
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2025-08-21
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Spot - Geospatial search for OpenStreetMap
Spot allows you to search for relations between entities in OpenStreetMap.
Spot can analyse your prompt for entities, their relations and location and display the matching results on a map.
DW Innovation
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2025-07-03
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Stadtspaziergänge ohne Hitze-Stress: Diese App navigiert dich durch den Schatten | heise online
https://heal.openrouteservice.org/#/place/@8.68112,49.410757,12
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2025-07-02
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Build 3D Scene Graphs for Spatial AI LLMs from Point Cloud (Python Tutorial)
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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
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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
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2025-01-07
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How AI Really Learns: The Journey from Random Noise to Intelligence
A Story of How Machines Learn to Think Through Language
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2025-01-06
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Fairness and Abstraction in Sociotechnical Systems
A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science - such as abstraction and modular design - are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.
Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, Janet Vertesi
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2025-01-06
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Projekt AUFGEHTS
Automatisiertes Verfahren zur Erstellung und Kalibrierung von Verkehrssimulationen auf Basis von Floating-Car-Daten
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2024-12-29
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Maps Mania: The AI Map Benchmark Test
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2024-12-21
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Map of walkable neighborhoods
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2024-12-17
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Maps Mania: Do You Live in 15 Minute City?
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2024-12-07
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The 15-Minute City
Let AI analyze any location worldwide based on the 15-minute city concept, where essentials like shopping, education, healthcare, transport, and leisure are within a 15-minute walk.
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2024-12-06
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The Spatial Edge (Newsletter)
Helping you become a better geospatial data scientist in less than 5 minutes a week. Click to read The Spatial Edge, by Yohan, a Substack publication with thousands of subscribers.
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2024-12-06
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Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with ImageRAG
Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 × 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size, the extensive spatial and contextual information that UHR images contain will be neglected. Otherwise, the original size of these images often exceeds the token limits of standard RSMLLMs, making it difficult to process the entire image and capture long-range dependencies to answer the query based on the abundant visual context.
In this paper, we introduce ImageRAG for RS, a training-free framework to address the complexities of analyzing UHR remote sensing imagery. By transforming UHR remote sensing image analysis task to image’s long context selection task, we design an innovative image contextual retrieval mechanism based on the Retrieval-Augmented Generation (RAG) technique, denoted as ImageRAG. ImageRAG’s core innovation lies in its ability to selectively retrieve and focus on the most relevant portions of the UHR image as visual contexts that pertain to a given query. Fast path and slow path are proposed in this framework to handle this task efficiently and effectively. ImageRAG allows RSMLLMs to manage extensive context and spatial information from UHR RSI, ensuring the analysis is both accurate and efficient.
Zilun Zhang, Haozhan Shen, Tiancheng Zhao, Yuhao Wang, Bin Chen, Yuxiang Cai, Yongheng Shang, Jianwei Yin
