What is prompt injection—an attack on the city model?

Building design
A computer screen displaying program code, symbolizing prompt injection and attacks on digital city models.
Code-Screen highlights security risks for AI and urban digital twins.

Prompt Injection: It sounds like hacker jargon from Silicon Valley—but it has long been a hot-button issue for anyone working with digital city models, artificial intelligence, and urban digital twins. After all, what happens if smart urban planning is suddenly manipulated from the outside? And how can planners protect themselves against targeted attacks on their digital tools? Welcome to the new reality of urban IT security!

  • What prompt injection is and why it poses a serious risk to city models
  • How urban digital twins and AI-based city models work technically—and how they become vulnerable
  • Specific attack vectors and real-world scenarios: From data manipulation to the sabotage of simulations
  • The role of open interfaces, data sovereignty, and governance in protecting against prompt injection
  • How planners, government agencies, and IT teams can counteract this threat with best practices and new security concepts
  • Why prompt injection is not only a technical problem, but also a cultural and ethical one
  • How our approach to AI and digital city models must change with regard to resilience and transparency
  • What the Future Holds: Risks, Opportunities, and the Requirements for the Role of Urban Planning Experts

Prompt Injection: When Attackers Manipulate City Models

Prompt Injection—the term originates from the world of artificial intelligence and refers to a specific form of manipulation of language models such as ChatGPT, Bard, or other generative AI systems. But what does this have to do with urban planning and digital twins? Quite simply: More and more cities and planning firms are relying on AI-powered analysis and simulation tools to tackle complex urban challenges. Whether it’s traffic management, climate resilience, or citizen participation—modern urban models are increasingly supported by algorithms that respond to inputs (“prompts”). This is exactly where prompt injection comes into play. An attacker deliberately injects manipulated inputs into an AI system to influence its behavior, extract sensitive data, or distort the results of simulations. It can start out harmlessly, such as with a faulty parameter in an open public participation tool. However, it can also become highly critical when entire traffic flows, evacuation routes, or energy demand forecasts are systematically falsified—and decision-makers act based on these falsified simulations.

Urban Digital Twins, in particular, are vulnerable to such attacks because they draw on a wide variety of data sources and provide external interfaces. Whether it’s geodata, sensor data, citizen feedback, or planning data—wherever inputs are processed, the potential danger of prompt injection lurks. The situation becomes even more dangerous when AI systems are used to automatically generate recommendations or even make decisions. In such cases, a successful attack can have far-reaching consequences for urban development—ranging from planning errors to the targeted sabotage of critical infrastructure.

It is currently difficult to answer the question of just how real the risk of prompt injection actually is. One thing is certain: the number of attacks on AI systems is rising worldwide, and the complexity of urban digital twins makes it easy for attackers to exploit hidden vulnerabilities. More and more cybersecurity experts are warning that cities with a high degree of digitization and open participation platforms are particularly attractive targets for targeted manipulation. And while protective mechanisms have been under development for years in traditional IT security, protection against prompt injection in the context of city models is often still in its infancy.

One reason for this is the rapid pace of technological development. While traditional attack patterns such as SQL injection or cross-site scripting are now well understood and can be effectively defended against, AI-based systems and their interaction models are still uncharted territory for many IT departments in public administration. Prompt injection is particularly insidious because it does not target technical vulnerabilities in the system’s core, but rather the semantic level of the inputs—that is, the actual content being processed. This means that every user who enters data or makes queries potentially becomes a gateway for attackers.

The implications for urban planning are serious. If digital twins can no longer be trusted, they lose their value as a basis for decision-making. The risk of algorithmic bias or even deliberate sabotage grows the more responsibility is delegated to AI-based tools. It is high time that planners and IT experts put the topic of prompt injection on the agenda—not merely as a side issue of digital transformation, but as a central element of urban resilience strategy.

Behind the Scenes: How AI and Digital City Models Really Work

To understand the risk of prompt injection, it’s worth taking a look into the technical and organizational depths of modern city models. Today, digital twins are more than just pretty 3D visualizations—they are networked, data-driven analytics platforms with diverse interfaces to sensor technology, planning tools, citizen participation, and administrative systems. At the heart of many applications are AI-based algorithms that recognize patterns, generate forecasts, or simulate scenarios based on the inputs. These algorithms work with so-called prompts—which are typically structured or unstructured text inputs that control the AI’s behavior. Here’s an example: An urban planner wants to know how a new construction project will affect traffic flows. He enters a corresponding query into the system, which then simulates different scenarios and provides a recommendation.

But what happens if an attacker manipulates this query? Prompt injection exploits precisely this vulnerability. Instead of a legitimate query, a manipulated prompt is fed into the system, causing the AI to produce incorrect or even harmful results. What’s particularly insidious is that prompt injection can also be used to extract confidential information from the system. A seemingly harmless prompt can trick the AI into revealing internal data that should actually be protected—such as sensitive planning data, citizen data, or information on critical infrastructure.

The technical foundations of modern urban models make it easy for attackers to exploit vulnerabilities. Open interfaces, APIs, web front ends, and participation platforms are indispensable from a digitalization perspective—they make urban planning more transparent, efficient, and participatory. But every interface is a potential attack vector. Especially in systems that rely on open data and citizen participation, there is a high risk that manipulated inputs will enter the system unnoticed and cause damage there.

Another problem is the complexity of the underlying data structures. Digital twins rely on a wide variety of data sources—ranging from geographic information systems and sensor networks to planning databases and social media. The AI must be able to interpret this data and link it together in a meaningful way. The more complex the system, the harder it becomes to identify and secure all entry points for prompt injection. On top of that, many AI models are regularly fed new data to stay up to date. Every data update and every new input is potentially vulnerable—making the task of securing the system an ongoing challenge.

Prompt injection also poses an organizational challenge. Who is responsible for the security of the city models? The city administration’s IT department? The external software service provider? The planners themselves? Clear lines of responsibility and defined processes are often lacking to detect and fend off attacks early on. Especially in municipalities where digital twins are being tested as pilot projects, the issue of IT security is often neglected. This must change—because as urban planning becomes increasingly digitized, so does the responsibility for protecting the digital infrastructure.

Attack Scenarios: Prompt Injection in Urban Practice

The theory sounds threatening, but what does prompt injection actually look like in practice? To understand this, it’s worth examining real-world attack vectors and potential scenarios that could have a massive impact on the daily work of urban planners, IT experts, and decision-makers. A classic example is the manipulation of participation platforms that rely on AI-based analysis of citizen feedback. Here, an attacker can deliberately feed in false information, for example by generating automated comments using specially crafted prompts. The AI interprets this data as legitimate feedback and derives recommendations for action—such as regarding traffic management, building density, or site selection for new infrastructure. The result: plans are made based on falsified data, and poor decisions are inevitable.

Another scenario involves the simulation of critical infrastructure. Modern urban digital twins simulate not only traffic flows but also energy supply, water management, and disaster response measures. If an attacker gains access to the AI’s input mechanisms, they can, for example, manipulate evacuation routes so that they lead to chaos in an emergency. Even more serious: In combination with social engineering, targeted disinformation campaigns could be launched that mislead citizens and government officials alike.

Sabotage of construction projects is also conceivable. Imagine a competitor or political opponent injecting manipulated parameters into the digital twin to negatively influence the simulation results of a planned large-scale project. Suddenly, the impacts on the urban climate or traffic appear much more severe than they actually are—with the aim of delaying or even preventing the project altogether. Without effective control mechanisms, such manipulations are often difficult to detect.

The situation becomes particularly critical when AI systems make decisions independently or control automated processes. In the field of traffic management, for example, manipulated prompts could lead to errors in the control of traffic light sequences, access regulations, or construction site coordination. The consequences range from increased traffic congestion to potential risks to traffic safety. Similar risks exist in the energy sector, for example, when load forecasts or maintenance intervals are adjusted based on manipulated simulations.

Finally, the risk of data extraction through prompt injection should not be underestimated. Attackers can trick AI systems into revealing confidential information that should actually be protected—ranging from planning documents and internal decision-making logs to personal data. This danger is particularly real on open participation platforms if it is not clearly defined which data the AI is permitted to process and output.

Resilience Through Governance: Protection Strategies Against Prompt Injection

Given these risks, the question arises: How can urban planners, government agencies, and IT teams protect their digital city models from prompt injection? The answer is multifaceted—ranging from technical measures to organizational and cultural changes. First and foremost, it is essential to establish a fundamental understanding of how AI systems work and where their vulnerabilities lie. Training and raising awareness among professionals are just as important as involving IT security experts in all phases of the development and use of digital city models.

From a technical standpoint, securing interfaces and input channels is the first step. Inputs should be validated, filtered, and checked for potential manipulation before they are processed by the AI. This involves the use of proven methods from IT security, such as input sanitization, authentication, and role-based access controls. Even more important, however, is the development of specific protective mechanisms against prompt injection—such as limiting interpretable command structures, monitoring inputs for anomalies, and using AI monitoring tools that automatically detect unusual behavior.

Another key factor lies in the governance of city models. Clear responsibilities, transparent processes, and regular audits of the AI systems in use are essential for identifying and addressing vulnerabilities early on. External penetration tests and red-teaming approaches can help realistically simulate attack scenarios and put the system through its paces. It is also important that all stakeholders—from city administrators to planners to software service providers—work closely together and jointly develop security guidelines.

Data sovereignty and open standards play a central role in protecting against prompt injection. When cities retain control over their data and rely on interoperable, open platforms, they can respond more quickly to new threats and adapt their systems. At the same time, participatory processes must be designed to make manipulation more difficult—for example, through user verification, plausibility checks on inputs, and transparent documentation of all decision-making processes.

Finally, a shift in mindset is needed when it comes to managing risks. Prompt injection is not merely a technical problem, but also a cultural and ethical one. Planners and decision-makers must recognize that digital city models will never be 100 percent secure. The goal is to build resilience—through redundancies, transparent communication, and a willingness to openly address errors and learn from them. Only in this way can urban planning remain trustworthy and sustainable in the digital age.

A Changing Professional Landscape: What Urban Planning Experts Need to Know Now

The phenomenon of prompt injection marks a turning point for the professional profile in urban planning, landscape architecture, and urban studies. It is no longer enough to simply master the traditional tools of spatial planning. Today, skills at the intersection of technology, governance, and ethics are in demand. Anyone working with digital city models must have a basic understanding of AI, data management, and IT security. This applies not only to IT specialists but to everyone who holds a position of responsibility in planning, administration, or consulting.

The ability to identify risks such as prompt injection and proactively counter them is becoming a central pillar of urban resilience. This includes a willingness to engage in continuous learning and to seek dialogue with experts from other disciplines. Cooperation and knowledge sharing are becoming increasingly important for identifying new threats early on and developing solutions together. Collaboration with external partners—from IT service providers to cybersecurity firms—is also gaining in importance.

Dealing with AI and digital city models also requires a high degree of transparency and a sense of responsibility. Decisions made on the basis of simulations and analyses must be documented in a traceable manner and communicated openly to the public. This builds trust and minimizes the risk that manipulations go undetected or that poor decisions are based on false assumptions.

At the same time, addressing prompt injection also presents opportunities. Those who build robust systems and processes can not only minimize risks but also enhance the innovative capacity and competitiveness of their city or organization. Security-conscious cities become pioneers in digital urban development—and set standards that others can follow.

Ultimately, the key insight is this: the digitization of urban planning is not a sure thing, but rather an ongoing learning process. Prompt injection is a symptom of the growing complexity of urban systems—and a wake-up call for everyone who wants to actively shape urban development. Those who wake up to this reality now can seize the opportunities of digital transformation without losing control.

Conclusion: Prompt Injection as a Litmus Test for the Future of Urban Planning

Prompt Injection is more than just a technical buzzword—it is the litmus test for the resilience, transparency, and innovative capacity of digital urban models. The increasing integration of AI, urban digital twins, and open participation platforms is making urban planning more efficient, participatory, and data-driven. At the same time, new vulnerabilities are emerging that enable targeted manipulation and sabotage. Anyone venturing into the urban future with digital tools must be aware of these risks—and actively counter them.

This requires technical expertise, strong governance structures, and a culture of open communication. Only when all stakeholders—planners, city administration, IT, and the public—work together on solutions can the digital transformation of urban planning succeed. Prompt Injection is here to stay—as a challenge, but also as an opportunity to continuously improve our own processes and systems. The cities that invest in security, transparency, and resilience today will be among the winners of the digital transformation tomorrow.

With that in mind: real-time urban planning remains science fiction—as long as we do not also take the risks of digital tools seriously. Those who build Urban Digital Twins are always also building urban resilience. Prompt Injection is the litmus test by which the future viability of modern urban models is measured. And only G+L addresses the challenges and solutions with such depth, criticality, and constructiveness. Stay vigilant—and stay innovative.

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Large Landscape Gardens: Fundamentals, History, and Significance

Building design
A landscaped garden and park complex designed around the theme of the Grand Landscape Garden
A street with flowers planted along a concrete wall—an example of urban greening. Photo: rumanamin/Unsplash

Anyone who enters a large landscape garden experiences nature as a work of art: vast lawns that gently give way to groups of trees, winding paths that continually draw the eye in new directions, bodies of water that reflect the sky, and sightlines that stretch across hills and valleys. The large landscape garden is one of the most influential inventions in European garden design—a design philosophy that, since the 18th century, has fundamentally redefined the relationship between people, nature, and space, and continues to shape open-space planning to this day.

  • What defines a grand landscape garden and how it differs from other types of gardens
  • How the landscape garden emerged historically and which intellectual movements shaped it
  • Which design principles and design elements underlie the landscape garden
  • What plant selection and vegetation techniques are characteristic of large-scale landscape gardens
  • How care, maintenance, and historic preservation are organized for historic landscapes
  • The significance of the landscape garden for contemporary urban development and open-space planning
  • What common misunderstandings and mistakes occur in planning and maintenance
  • How large-scale landscape gardens are being reevaluated in the context of climate adaptation and biodiversity

Definition and Classification: What Is a Large Landscape Garden?

The large landscape garden, known in English-speaking countries as a “landscape garden” or “English landscape garden,” is a type of open space that deliberately eschews the formal, geometric garden design of the Baroque and Renaissance periods and instead takes an idealized natural landscape as its design basis. It is characterized by meandering paths and shorelines, freely shaped bodies of water, irregularly arranged groups of trees and shrubs, expansive lawns, and a carefully staged sequence of spatial impressions. The word “large” refers not only to the area—which in fact often spans several hundred to over a thousand hectares—but also to the conceptual aspiration: the garden as a reflection of a complete, internally coherent landscape.

The large landscape garden differs fundamentally from the Baroque palace garden in its design logic. While the Baroque garden controls and organizes nature through symmetry, axes, parterres, and topiary (the pruning of trees and shrubs into geometric shapes), the landscape garden simulates the random, the organic, and the picturesque. This simulation, however, is by no means random: It is based on precise design decisions and the deliberate use of topography, vegetation, water, and architecture to create specific moods, visual relationships, and spatial experiences. In this sense, the landscape garden is a highly refined art form that conceals its methods.

Typologically, the large landscape garden can be distinguished from smaller landscape-style home gardens, 19th-century public parks, and modern city parks, even though all these types have emerged from it or been influenced by it. The decisive difference lies in scale and intention: the large landscape garden is a total work of art that reinvents an entire landscape, whereas the 19th-century city park primarily served social and hygienic purposes, and the landscape-style private garden merely borrows formal elements.

Historical Origins: England, the Enlightenment, and Criticism of the Baroque

The roots of the grand landscape garden lie in early 18th-century England, where a combination of philosophical, literary, and political currents gave rise to a new aesthetic of nature. The Enlightenment, with its trust in reason and the observation of nature, stood in productive tension with the contemporary longing for the sublime and the picturesque—that is, for landscapes that evoke awe, vastness, and irregularity. Poets such as Alexander Pope, philosophers such as Anthony Ashley Cooper, the third Earl of Shaftesbury, and painters such as Claude Lorrain and Nicolas Poussin—whose landscape paintings served as direct models—shaped the aesthetic program of the new garden style.

The gardens at Stowe (Buckinghamshire), Stourhead (Wiltshire), and Rousham (Oxfordshire) are considered key early examples. At Stowe, Charles Bridgeman, William Kent, and finally Lancelot “Capability” Brown—who would become the most influential landscape architect of the 18th century—worked in succession. Brown earned his nickname “Capability” from his habit of identifying the “capabilities” of a site for improvement. His style, characterized by meandering lakes, gently undulating lawns, clusters of trees, and belts of trees along the edges, became the epitome of the English landscape garden and influenced hundreds of gardens throughout Great Britain.

At the same time, a theoretical debate developed about the picturesque, led primarily by William Gilpin, Uvedale Price, and Richard Payne Knight. These authors criticized Brown’s style as too smooth and too orderly and called for a rougher, wilder garden aesthetic more closely aligned with painting. This debate is important for understanding the grand landscape garden because it shows that the style was not a uniform doctrine but a dynamic field of aesthetic debate.

The landscape garden style reached Germany in the second half of the 18th century, initially through travelogues and publications, then through direct commissions of English gardeners and the work of local designers such as Friedrich Ludwig von Sckell, who laid out the English Garden in Munich, and Peter Joseph Lenné, who had a decisive influence on the Potsdam park landscape surrounding the New Garden and Sanssouci Park. Prince Franz von Anhalt-Dessau created the Wörlitz Park, the first significant landscape garden project on German soil, which is now a UNESCO World Heritage Site. In these German gardens, the English landscape style is often combined with Enlightenment-era educational programs: temples, inscriptions, monuments, and symbolic structures transform the garden into a walk-through total work of art with a moral and political message.

Design Principles and Design Elements of the Grand Landscape Garden

The grand landscape garden employs a range of design elements that, when combined, create its characteristic spatial experience. The most important element is the staging of visual relationships: the path through the garden is a sequence of images that the designer has deliberately composed. Axial views open up onto temples, bridges, or bodies of water; groups of trees and shrubs frame vistas and block unwanted views; hills and depressions create moments of surprise when, after an ascent, a sweeping view suddenly opens up.

Water plays a central role. Natural or artificially created lakes and ponds with irregular, curved shores are a hallmark of the style. Capability Brown was famous for transforming rivers into expansive lakes through damming, which gave the impression of natural bodies of water. The reflection of the sky and the surrounding vegetation on the water’s surface significantly enhances the sense of depth in the space. Bridges, islands, and shoreline plantings complement the body of water as design elements.

Vegetation is conceived not as individual plants but as mass and volume. Solitary trees—that is, freestanding individual trees with distinctive shapes or sizes—provide focal points within the lawn. Groups of trees and shrubs of varying sizes and compositions structure the space and create a layering of foreground, middle ground, and background reminiscent of landscape painting. Belts of trees and shrubs along the garden’s edges enclose the grounds from the outside and create the impression of a self-contained world. The lawn itself, referred to in the original English as “lawn,” is not merely leftover space but a design element: it connects the groups of trees and shrubs, guides the eye, and gives the garden its sense of expansiveness.

Architectural features—such as temples, grottoes, ruins, bridges, obelisks, and similar structures—complete the picture. They are not functional buildings but rather convey meaning: they allude to ancient models, literary themes, or the client’s worldview. Ruins, whether genuine or artificially created, are a particularly characteristic element because they symbolize transience and the superiority of nature over human creation—a central theme of Enlightenment aesthetics.

Plant Selection and Vegetation Design in the Landscape Garden

The use of plants in large landscape gardens follows different principles than in formal gardens. The focus is not on the individual plant with its botanical characteristics, but on the effect of the mass of plants within the space. For woody plantings, native tree species were and continue to be used predominantly; in Central Europe, these include oak, beech, hornbeam, linden, ash, maple, and alder, supplemented by introduced species such as plane tree, horse chestnut, and various conifers, which have been widespread in European landscape gardens since the 18th century.

Creating woody plantings in the landscape garden style requires thinking in terms of long time frames. A tree planted today as a solitary specimen will not fully unfold its spatial impact until decades later. Capability Brown is said to have remarked that he worked for posterity, not for the present. This temporal dimension is a fundamental difference from architecture: the landscape garden is never finished; it changes continuously through the growth, aging, and loss of trees and shrubs. Professional care and maintenance must guide this process without destroying the original design concept.

The lawns of historic landscape gardens were originally maintained through grazing by sheep and cattle, which also served an ecologically valuable purpose. With the invention of the lawn mower in the 19th century and the increasing mechanization of maintenance, maintenance practices changed fundamentally. Today, those responsible for maintenance face the challenge of balancing the preservation of the historic character, the demands of visitor use, and the goals of promoting biodiversity. Extensive meadow management with infrequent mowing, the creation of wildflower strips, and the promotion of old-growth wood in groups of trees and shrubs are strategies that are increasingly being incorporated into modern maintenance concepts for historic landscape gardens.

The introduction of exotic woody plants, particularly rhododendron species and conifers from North America and East Asia, was a significant expansion of the plant repertoire in the 19th century. Many historic landscape gardens today contain significant collections of these species, which have since acquired historical value in their own right. At the same time, Rhododendron ponticum has become an invasive problem in Atlantic-influenced regions of Great Britain and Ireland, threatening the ecological integrity of historic sites. This tension between historical authenticity and ecological responsibility is a central theme in today’s preservation of landscape gardens.

Maintenance, Preservation, and Management Concepts for Historic Sites

Historic landscape gardens pose special challenges for heritage conservation because their heritage value lies not in solid materials but in a living, ever-changing composition of vegetation, topography, water, and architecture. The challenge lies in preserving the spirit of the original design without freezing the growth of the vegetation or reducing the site to an artificial state. Maintenance concepts for historic landscape gardens must therefore be based on a careful historical analysis that evaluates sources such as original plans, paintings, engravings, travelogues, and plant lists.

In Germany, historic garden conservation has established itself as a distinct discipline situated at the intersection of landscape architecture, historic preservation, and garden history. The Florence Charter, a document adopted in 1981 by the International Council on Monuments and Sites (ICOMOS), defines principles for the preservation of historic gardens and parks. It emphasizes that the historic garden is a living monument that requires continuous care, and that restoration measures must be based on documented sources.

Practical maintenance tasks in historic landscape gardens include the regular inspection and care of tree and shrub populations, the preservation and restoration of water features and ornamental structures, the maintenance of lawns and paths, and the management of visitor flows. The latter is a particularly complex task in heavily visited sites such as the English Garden in Munich or Sanssouci Park in Potsdam, where user needs, historic preservation, and ecological goals must be balanced. Foot traffic, soil compaction, and damage to the root systems of old trees caused by visitors are real threats that are mitigated through route guidance, signage, and, if necessary, temporary closures.

Significance for Contemporary Open-Space Planning, Climate Adaptation, and Biodiversity

The large landscape garden is not a museum relic, but rather a reference model that remains highly relevant for today’s open-space planning and urban development. Its fundamental principles—the linking of open spaces into contiguous green spaces, the use of topography and water as design elements, and the creation of spatial diversity through the alternation of open and enclosed areas—are reflected in modern concepts such as the Green Belt, Green Infrastructure, and the 21st-century city park.

In the context of climate adaptation, this type of landscape garden offers specific advantages. Extensive stands of trees and shrubs create cooling effects through evaporation and shading, which are increasingly in demand in overheated cities. Extensive lawns and water bodies help regulate the urban water cycle by absorbing, temporarily storing, and gradually releasing rainwater. The topography of many historic landscape gardens—with hollows, depressions, and ponds—corresponds in its basic structure to the principle of the “sponge city” (a concept of water-sensitive urban planning), which is currently being discussed as a response to increasing heavy rainfall events.

For biodiversity, large-scale landscape gardens—with their stands of mature trees, extensive lawns, bodies of water, and wooded edges—serve as valuable habitats. Old oaks, beeches, and linden trees, with their hollows, cracks, and dead branches, provide habitat structures for bats, woodpeckers, beetles, and numerous other species that can hardly find a place in intensively used urban landscapes. Promoting deadwood, adopting a more extensive approach to lawn care, and creating riparian buffer zones are measures that can enhance the ecological value of historic landscape gardens without compromising their character.

At the same time, open-space planning must critically reflect on what the grand landscape garden historically also was: an instrument of land acquisition and social segregation. Many of the most significant English landscape gardens were created through the enclosure and transformation of common land—a process that displaced villages and dispossessed farmers. This socio-historical dimension is essential to a complete understanding of the type, even if it does not negate its aesthetic and ecological value.

The Grand Landscape Garden as an Enduring Design Model

The large landscape garden established a design philosophy that extends far beyond its historical context of origin. The idea that open space is designed not through geometry but through reflection on spatial sequences, visual relationships, movement, and atmosphere remains a fundamental principle of landscape architecture to this day. Anyone planning a city park, a park complex, or a large-scale open space works—consciously or unconsciously—with techniques developed in 18th-century English landscape gardens.

The strength of this typology lies in its adaptability. The large landscape garden can serve simultaneously as a recreational space, a nature conservation area, a climate buffer, a cultural heritage site, and a source of urban identity. It requires its planners and caretakers to think in the long term, to understand ecological processes, and to exercise aesthetic judgment that transcends short-term trends. This combination makes it a challenging yet rewarding field of practice for landscape architecture.

The great historic gardens—from Stourhead to Wörlitz to the English Garden in Munich—are not only testaments to a bygone era of garden art, but also living laboratories where one can observe how vegetation, topography, water, and architecture have interacted over the centuries. Anyone who walks through these gardens with a knowledgeable eye learns more about open-space planning than from many textbooks. The grand landscape garden remains what it has been since its inception: a design model of extraordinary depth and durability.

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Photo: Screenshot Instagram/ecpodcast

Museums are not the only ones using podcasts to introduce themselves. Conservation science is also discovering this medium. Students at the University of Amsterdam have now launched a conservation podcast The new podcast format “ECPodcast – by and for Emerging Conservation Professionals”, which five Master’s students at the University of Amsterdam have developed in recent weeks, is just over two weeks old. The series […]

Museums are not the only ones using podcasts to introduce themselves. The restoration sciences are also discovering this medium. Students at the University of Amsterdam have now launched a restoration podcast


Studierende der Universität Amsterdam haben jetzt einen Restaurierungs-Podcast gelauncht Foto: Screenshot Instagram/ecpodcast
Students at the University of Amsterdam have now launched a restoration podcast
Photo: Screenshot Instagram/ecpodcast

The new podcast format “ECPodcast – by and for Emerging Conservation Professionals”, which five Master’s students at the University of Amsterdam have developed in recent weeks, is just over two weeks old. The series provides insights into the “Conservation & Restoration” course at the University of Amsterdam. In the first episode, Mané van Veldhuizen, Anthi Soulioti, Willemijn Bolderman, Terri Costello and Liz Hébert talk about their specializations and report on their recruitment test. They have now been at university for a year. The second episode is already online – here the students talk to Professor Ellen Hendriks, who is involved in the program’s application process: How did Covid-19 affect the selection week? Just listen in: www.ecpodcast.de