


































85 

GeoPlanning 
  Journal of Geomatics and Planning                                                                                                                   Vol. 8, No. 2, 2021     
 

Original Research 

Trends in The Adoption of New Geospatial 

Technologies for Spatial Planning and Land 

Management in 2021 

Walter T. de Vries 1* 

1. Technical University of Munich, Germany 

DOI: 10.14710/geoplanning.8.2.85-98  

Abstract 

Changes in spatial planning and land management practices, regulations and operations have frequently relied on the uptake 

of innovations in geospatial technologies. This article reviews which ones the spatial planning and land management 

domains has effectively adopted and which new ones might potentially disrupt the domain in the near future of 2021 and 

beyond. Based on an extensive concept-centric trends synthesis and meta-review, the analysis demonstrates that whilst 

geospatial technologies are clearly gaining wider societal recognition and while private companies are indeed developing 

promising applications, its adoption in office work of public officials and public decision makers remains almost as limited 

as before. The potentially most disruptive technologies for the domain are however BIM, Block chain and Machine learning. 

Copyright © 2021 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1.  Introduction 

Although geospatial technologies have changed continuously in the past 30 years, the uptake, adoption 

and integration of these in spatial planning and land management practices, regulations and agencies have not 

always been effective and lasting. Since the emergence of geographic information systems (GIS) and the uptake 

of remote sensing technologies in spatial planning and land management literature, one can only conclude that 

some technological advancements and conceptualisation artefacts have been more persuasive than others have. 

This has partly to do with natural evolution and adoption (or the lack thereof) of technologies in general, but 

also with the specific nature and demands of spatial planning and land management practices, regulations and 

agencies at large.   

This article poses three questions: Which geospatial technologies do spatial planning and land 

management practices, regulations and agencies currently (in 2021) effectively employ and integrate?; What are 

the geospatial technology trends of 2021 which have the potential to change (or even disrupt) spatial planning 

and land management practices, regulations and agencies significantly?; Which evidence, artefacts and 

manifestations exists that spatial planning and land management practices, regulations and agencies are 

fundamentally changing because of these technologies?   

This article first describes the boundaries of the conceptualisations of geospatial technologies on the one 

hand and spatial planning and land management practices, regulations and agencies on the other.  It then 

explains how this research is addressing each of the questions within the scope of this paper.    

 

 

e-ISSN: 2355-6544 
 
Received: 5 August 2021;  
Accepted: 1 December 2021;  
Published: 30 December 2021. 
 
Keywords:  
Geospatial technologies; Spatial 
planning; Land Management 
 
*Corresponding author(s) email: 
wt.de-vries@tum.de  
 
 

 

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1.1.  Conceptualising Geospatial Technologies for Spatial Planning and Land Management  

The term geospatial technologies is concrete and ambiguous at the same time. This paper distinguishes 

the following functional categories of geospatial technologies that are relevant for spatial planning and land 

management.  

Integrative and analytical technologies. These include Geographic information systems (GIS), in the 

form of proprietary systems, or through web based or open source based systems. Goals of these technologies 

have always been to detect and deduct locations, spatial patterns and spatial clusters on the one hand, and to 

register, record, allocate, adjudicate and assign properties to spatially distributed artefacts and objects on the 

other hand.     

Data acquisition and data processing technologies. These include all surveying, photogrammetry and 

remote sensing technologies at large. Increasingly these technologies converge, especially with more cloud and 

point based systems. Goals of these type of technologies have always included establishing reliable and accurate 

georeferenced foundation data and associated geodetic networks, geometric descriptions and classifications of 

objects and changes in objects, and to acquire systematically and dynamically georeferences during or for 

navigation purposes.   

Smart and artificial intelligence technologies. This overarching category refers to technologies, which 

generate new results and scenarios and also can independently and autonomously derive and execute decisions. 

In addition to the more conventional spatial decision support systems (SDSS) and planning support systems 

(PSS), these include autonomous sensor and surveillance technologies, machine learning and artificial 

intelligence.    

Visualisation, representation and simulation technologies. These type of technologies are both 

constructing data models and converting these into graphic static and dynamic images and other types of 

representations, which provide a more comprehensive perspective on a particular matter, or a set of phenomena. 

Besides the conventional cartographic visualisation technologies to display objects and processes in 2D, 3D, or 

4D, these include virtual, augmented, immersive and mixed reality, and new types of hardware such as decision 

support tables, hologram tables and head mounted displays in order to visualise, feel, touch, hear and perceive 

dynamic simulated environments.    

Data management technologies. These technologies structure and store data in such a manner that their 

inter-relations can be easily accessed, queried and analysed. Traditionally these referred to relational or SQL-

based (geo) databases, but recently also non-relational or NoSQL data management technologies have advanced. 

These include graph stores, column stores, key value stores and document stores.  Additionally, data 

architectures and access technologies have evolved, culminating in for example decentralised blockchain 

architectures.  

Spatial planning and land management practices, regulations and agencies encompass all activities, 

decisions, government and non-government organisations, guided and unguided behaviour which have the aim 

to intervene in socio-spatial and bio-physical artefacts, constructions and relations which are needed to benefit 

from the land, housing and shelter.  (de Vries, 2018a) would refer to these encompassing people-to-land/space 

interventions as fundamental changes, which are both functions of and dependent relations of the respective 

changes in governance, law, social-spatial relations, economic opportunities and dependencies, perceptions and 

beliefs and behaviour. These chances are also visible in the manner in which functions of spatial planning and 

land management are currently carried out.  There are various types of functions and aims of spatial land 

interventions, and the execution of interventions typically takes place in both a consecutive, iterative and 

integrated manner (GIZ, 2012; Metternicht, 2018). Table 1 provides an overview of such functions (including 

spatial structure and design, spatial monitoring, administration of land and properties and compliance and 

coercion) a number of article references which highlight ongoing or recent changes in how these functions are 

carried out.  

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Table 1. Functions and aims of spatial land interventions 

Spatial planning and/or land 

management function 
Examples 

Recently described in -amongst 

others- 

Spatial structure and design (City) Master planning (Li et al., 2021) 

Physical planning (Bakır et al., 2018) 

Spatial localization  (Pokonieczny, 2016) 

Land use zoning (PU et al., 2013) 

Land consolidation (Demetriou, 2018) 

Land redistribution (Hentze and Menz, 2015) 

Urban development boundaries (Liu et al., 2017) 

Urban form and shape (Williams, 2017) 

Spatial monitoring and 

assessment 

Land use change detection  (Wang et al., 2020) 

Urban growth (Setyono et al., 2016) 

Urban greening (Heckert and Rosan, 2018) 

Urban blight (Mireku, 2020) 

Vacancy of houses / unused land (Zou and Wang, 2020) 

Informal settlements growth (Estoque and Murayama, 2015) 

Risks and impact assessments (Buchori et al., 2018) 

Land encroachment (Thapa and Bahuguna, 2021) 

Administration of 

land/properties  

Land registration (Budiman, 2020) 

Land recordation (Chipofya et al., 2021) 

Land valuation and pricing  (Elmanisa et al., 2017) 

Spatial / land restrictions  (Kitsakis and Dimopoulou, 2017) 

Communal, customary  tenure (Chigbu et al., 2021) 

Land grabbing (Petrescu et al., 2020) 

Conservation of cultural heritage (Pepe et al., 2020) 

Compliance and coercion Housing permit compliance (Offei et al., 2018) 

Sanctions and penalties (Boodhoo, 2021) 

Evictions and relocations (Desai et al., 2018) 

Participation and mobilisation Stakeholder needs analysis (Giuffrida et al., 2019) 

Handling of complaints (Dhini et al., 2017) 

Collaborative design  (Jankowski et al., 2021) 

Community participation  (Kusmiarto et al., 2020) 

 

A few comments to explain and describe the content of the referred articles and associated changes in 

functions in Table 1. Spatial structure and design encompasses both finding the right location for new structures 

as well as the spatial allocations of land (use) rights, restrictions or responsibilities. Geospatial technologies can 

typically support these activities by querying and modelling spatial phenomena with the purpose to create a 

rational design in desired or anticipated land use outputs or spatial forms. Spatial monitoring is an evaluation 

and assessment type of activity, which is usually needed to measure the degree of progress of a spatial policy 

intervention. Geospatial technologies typically support the measuring and clustering of variations in spatial 

phenomena. Land and property administration is a branch of spatial planning and land management which 

records and registers relations between subjects and objects, in terms of rights, restrictions, responsibilities, 

values, development activities. Often this sector relies on robust relational (geo) databases and domain models. 

Compliance and coercion functions refer to the policing and regulatory actions leading to an intervention by 

force or by penalties. Participation and mobilisation is a typical activity of both spatial planning and land 

management, which connects political and societal goals and needs to spatial planning and land interventions. 

Typically, those geospatial technologies, which are available, accessible and operable for all citizens at all levels 

and registers of society, could support this activity.   

 

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2. Data and Methods 

In order to reveal which technologies have become mainstream in spatial planning and land management 

practices, regulations, for the analysis of where and how the new geospatial technologies may disrupt spatial 

planning and land management practices and agencies and in order to describe, highlight and synthesize current 

2021 trends in geospatial technologies we have relied on: a concept-centric summary of cited evidence and 

referrals from scientific literature (journals) and geospatial conferences. The selection of journals was based on 

the listed GIS and RS journals by (Biljecki, 2016), added with the list of https://3d.bk.tudelft.nl/journals/ on 

the one hand, and an internal list of land management journals maintained by the Chair of Land Management at 

TUM; a selection from relevant conferences, grey literature and strategic (national) policy documents.  These 

include the land related and geospatial technology conferences (such as FIG, ISPRS, PLPR, EALD, RSA, 

AGILE); and a synthesis of various systematic government information sites (e.g. NOAA), review papers, 

geospatial magazines (e.g. GIM International, Geospatial World), yearly or regular trend watcher blogs and 

opinion pieces in relation to geospatial technologies. The aim was to select manuscripts and electronic sources 

published in 2014 or later, which connect geospatial methods to specific functions of land management and spatial 

planning.   

 

3. Result and Discussion 

3.1. Currently employed geospatial technologies in spatial planning and land management practices, 
regulations and agencies 

Table 2 shows the synthesis of historical references and review papers, describing what sort of geospatial 

technologies and algorithms have been used for in relation to spatial planning and land management activities 

and models. Table 2 is by no means complete or fully inclusive. The emphasis in the selection of examples has 

been to display the variety and broadness in both the technologies and the applications. As such, the references, 

which represent specific studies connecting the technologies to specific applications, are also exemplary. Still, 

however the table 2 provides a summary of which technologies have become mainstream in spatial planning and 

land management practices, regulations and agencies.   

What is obvious is that the combination of (open) GIS and the embedding of different kinds of data models 

has become conventional and fully accommodated in different phases and functions of spatial planning and land 

management processes. This enables the development of geoweb applications with tools such as the JavaScript 

Openlayer APIs, Geoext, Leaflet, and with OpenLayer API as the development environment for Geo Web 2.0 

software applications.  GIS servers such as Geoserver, Mapserver, and DEGREE are supporting the distribution 

of spatial data into various web services formats such as Web Mapping Services (WMS). PostgreSQL with an 

extension of POSTGIS provides the open source object relational database system. Finally, models such as (City) 

GML and LADM are addressing the problems of geospatial conventional data model standards, such as the 

disconnect between different geometric representations for the same objects and processes.    

Some words of caution and reflexivity are nevertheless necessary for the adoption of open source 

technologies in combination of big data. Vast amounts of geospatial literature tends to remain focused on the 

technical modelling and simulation aspects of the physical spatial environment and not so much on the political, 

discretionary  and behavioural aspects of the social spatial environment which are also crucial for spatial planning 

and land management. An exception to this are the agent-based modelling (ABM) techniques, model and evaluate 

dynamic behaviour. In essence, ABM simulates complex systems through detailed assumptions in behaviour and 

interactions of people, animals or vehicles (Kieu et al., 2020), and it has therefore been applied in for example 

urban traffic simulation, disaster responses and evacuations. In combination with data assimilation techniques, 

which provide continuous updates with real-time data, real-time forecasts and predictions improve.   

 

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Table 2. Mainstream geospatial technologies in spatial planning and land management 

Type of function  Examples of technologies, algorithms Type of applications References 

Integrative and 

analytical  

(Open) GIS City master planning (Gong et al., 2014) 

Digital Surface Model (DSM) Land use change detection  (Asokan and Anitha, 2019) 

Object based nearest neighbour Land cover change detection (Aslami and Ghorbani, 

2018) 

Rational Polynomial Coefficients (RPCs) Urban built-up expansion (Prakash and Bharath, 

2020) 

Discrete mathematics, migrating bird 

algorithms 

Land redistribution  (Tongur et al., 2020) 

Data acquisition  Multispectral image processing Land monitoring, land 

conservation 
(Radočaj et al., 2020) 

GNSS   

Auto Correlation Function (ACF) change 

detection method 

Detecting human settlements (Kleynhans et al., 2015) 

Smart and 

artificial 

Cellular automata Urban flood modelling (Ahmed et al., 2018) 

Agent-based modelling  (Mustafa et al., 2017) 

Visualisation and 

simulation  

Urban SIM modelling Urban transportation 

expansion 

(Di Zio et al., 2010) 

3D digital photogrammetry City modelling and 

visualisation 

(Litwin et al., 2017) 

Geometric modelling Urban expansion (Purevtseren et al., 2018) 

Virtual reality Participatory planning (Meenar and Kitson, 

2020) 

Data models and 

management   

Relational data domain models Land administration (Pržulj et al., 2019) 

Open source databases (e.g. 

Postgresql/PostGIS) 

 (Teja et al., 2020) 

(City) GML, LADM City management (Beil and Kolbe, 2017) 

 

3.2. Potentially disruptive geospatial technology trends of 2021 

Various research review papers, blogs and opinion pieces summarize the 2021 trends and developments in 

the geospatial technologies landscape and refer to a distinct selection of technologies as being disruptive. We 

define ‘disruptive’ here as drivers and changes, originating from technological innovations which displace and 

replace existing socio-organizational structures and workflows, interpersonal and inter-institutional relations, 

utilization of technologies, and societal situations (de Vries et al., 2020). This implies that not every technology 

is disruptive, but only those, which result in fundamental, and lasting changes. The trend watchers are 

particularly interested in those technologies, because they also provide new market shares and revenues 

(Abdullah, 2021a; Richardson, 2017; GeoCTRL, 2021). Table 3 provides a synthesis of the potentially disruptive 

geospatial technologies.  

With regard to the integrative and analytical technologies one can argue that the technologies such as 

Building information modelling (BIM) and opensource GIS , and the emergence of big and linked data are not 

new, as they have been existing within separate technological domains. However, the volume of the uptake and 

the persuasive embedding of these technologies are starting to disrupt and fundamentally change the processes 

and structures in which they are used. One of such disruptions concerns the adoption of cloud computing 

solutions, in the form of Cloud computing SaaS (Software as a Service), in particular for geospatial applications. 

GIS as SaaS provides Cloud based mapping tools, open data platforms, AI integration, geospatial data editing 

and sharing and helps handling big data. Current geospatial cloud services provide ready-to-use geospatial 

datasets and images whereby users can conduct different types of analyses at a variety of geographic scales. 

Companies like ESRI, Google Maps (Google), Bing Maps (Microsoft), Super Map, Zondy Crber, GeoStar, 

Hexagon Geospatial, CARTO and GIS Cloud are participating in GIS Cloud computing technology. Companies 

such as Amazon (AWS), Google (Google Earth), and Microsoft (Bing Maps) are already providing these 

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information architectures for the past 10 years, but the  Google Earth and Bing Maps  mapping tools are not 

suitable for large enterprise-wide GIS applications. Instead, a geospatial cloud, providing GIS as SaaS is able to 

give many analytic and visualisation capabilities and ready to use map or imagery layers. Integrated with AI and 

machine learning, the GIS cloud can automate techniques like classification, change detection, clustering etc. 

The extensions of Saas are PaaS and IaaS, i.e.‘ Platform as a service‘ and ‘Infrastructure as a service‘. SaaS delivers 

applications without downloading or installation (e.g. Google Apps, Dropbox and Concur). PaaS provides a 

framework for developers. It is built on virtualization technology ( e.g Windows Azure, Google App Engine). 

IaaS gives infrastructure to organisations. In IaaS resources are available as a service (e.g.  Microsoft Azure, 

Amazon AWS, Digital Ocean etc.).  

 

Table 3. Functional categories of potentially disruptive geospatial technologies 

Type of function  Examples References 

Integrative and analytical  BIM connected to GIS (Kaden et al., 2020; Goyal et al., 2020) 

Geospatial analytics (Lin et al., 2020) 

Big and linked geospatial data (Werner and Chiang, 2021) 

Data acquisition  (Open) LiDAR (Ye et al., 2020) 

Drone technologies (Yunus and Azmi, 2020) 

Miniaturized sensors  

Smart and artificial Machine and deep learning (Muhammad et al., 2021) 

Pattern recognition   

Bayesian network modelling (Marcot and Penman, 2019) 

Visualisation, representation and 

simulation  

Digital twins (Ketzler et al., 2020) 

Mapping as service (Abdullah, 2021b) 

CityGML3.0 (Kutzner et al., 2020) 

Extended, immersive  and mixed reality (Çöltekin et al., 2020) 

Data management   Blockchain (Verheye, 2020) 

noSQL (Bennett et al., 2019) 

Cloud computing  

Graph databases (Zheng et al., 2017) 

Data warehousing  

 

The branch of geospatial analytics extend the application of GIS functionalities. In addition to relying on 

traditional maps and georeferenced objects, Geospatial analytics uses data from all kinds of technology, including 

location sensors, social media, mobile devices, satellite imagery. The main purpose of geospatial analytics is to 

build data visualizations for understanding phenomena and finding trends in complex relationships between 

people and places, in order to make predictions on socio-spatial and bio-physical spatial changes easier and more 

accurate. Examples of where geospatial analytics may become useful include making more informed choices 

about building or expanding facilities, speeding up logistics by running routing scenarios, finding patterns of 

criminal activity within a region, or minimizing risks from  hazardous location-based events like powerful storms 

(USC (University of Southern California), 2021).  

Specifically for the domains of spatial planning and land management the role of BIM connected to GIS is 

crucial. The Open Geospatial Consortium (OGC), supported by buildingSMART International (bSI) are now 

preparing an initiative to explore geospatial and BIM data integration based on meaningful real-world use cases. 

So far, the two communities rely on different data modeling approaches with respect to fundamental concepts, 

semantics, access, level-of-detail, and several other aspects. The next step is however to verify how to connect 

and integrate  the geospatial  open standards such as CityGML, LandInfra/InfraGML, IndoorGML, and IMDF 

with the BIM open standards such as  IFC (Industry Foundation Classes), ISO19650, and the openCDE API 

portfolio, such that digital models for the built environment can be interchanged.  

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Of particular interest in the emerging data acquisition technologies is Light detection and ranging 

(LiDAR) technology.  Compared to using traditional stereophotogrammetry relying on 2D aerial photos or 

images images to generate a 3D digital terrain model, LiDAR creates such a digital terrain model using a large 

amount of points collected by a laser, a scanner, and a specialized GPS receiver. Although the raw data are 

discrete-return, classified point-cloud data provided in LAS format, LiDAR data products are often created and 

stored in a gridded or raster data format. The raster format can be easier for many people to work with and also 

is supported by many different commonly used software packages. Originally designed for 3D terrain mapping, 

the range of applications relevant for spatial planning and land management is growing fast, including coastal 

floodplain mapping, forest and green area mapping, hydrological assessments, landscape ecology, urban 

planning, survey assessments, volumetric calculations of buildings and constructions, and design and evaluation 

of coastal engineering structures (NOAA, 2021). Point-cloud data acquisition, such as Lidar, and the variety of 

drones have significantly altered and extended the data acquisition techniques. Abdullah (2021b) describes an 

increasing uptake of Lidar due to improvements in lidar data density, quality and accuracy. Pauschinger and 

Klauser (2020) list both public users of drones (such as emergency services, police, archaeology and urban 

planning), and private ones (such as filmmaking, security, land surveying, infrastructure development and 

agriculture).   

For the development of smart cities and regions, the machine learning community applies artificial neural 

networks. Deep learning is a type of machine learning, which is a subset of artificial intelligence. Deep learning 

can analyze images, videos, and unstructured data in ways machine learning can’t easily do. Muhammad et al. 

(2021) provide a taxonomy of currently available deep learning methods applied in smart city development and 

discovered that generally the use of convolutional neural networks are highly popular in deep learning based 

smart city applications. The applications of deep learning algorithms are especially in the domains of road, 

transportation and mobility management, but also emerging in monitoring of air pollution and real estate 

management.  The major disruption related to smart and artificial technologies is the fact that and increasing 

number of people are ‘plugged in’ as compared to ever before. This allows for smart tech solutions which are 

more effectively targeting spatial planning and land management issues in real time, due to the vast amount of 

active and passive data  generation, which is stored and analysed by interconnected systems (Brode 2021).  

 In the field of visualisation, representation and simulation technologies one can observe many changes 

and improvements, such as digital twins, mapping as service CityGML3.0 and extended, mixed and immersive 

reality. Digital twins are the digital surrogate, replica or representation of a physical object, process or service.  

These can include specific objects, such as buildings or wind mills, but also represent larger and abstract objects, 

such as projects sites or entire cities. Representing these objects and phenomena in a digital environment, 

connected with digital programs, models and algorithms enables predictions and simulations of how changes or 

interventions play out (without an actual intervention or disturbance).  Kutzner et al. (2020) describe how the 

CityGML version 3.0 has extended its core modules with the new modules Construction, Versioning, and 

Dynamizer, as well as the revised Building and Transportation modules. Common in all the new representation 

techniques is that one can more easily than before simulate, experiment, test and visualise expansions, risks, 

movements and behavioural scenarios. The concepts related to extended realities, referred to as XR, is an 

umbrella term for the virtual, augmented, and mixed reality (VR, AR, MR) refer to technologies and conceptual 

propositions (Çöltekin et al., 2020). The technologies do not only help to envision alternative scenarios, especially 

relevant when planning cities or landscapes, but can even change people’s realities, as most of these systems are 

interactive and with cognitive effects and impacts.    

The changes in data handling and management technologies particularly address the limitations of 

relational databases.  Blockchain technologies are particularly well equipped to address transparency, access and 

accountability problems, which are often tied to centralised relational databases.  This type of technology is 

particularly suitable for applications whereby regular transactions take place and whereby these transactions 

need to be accurate and systematically monitored. As such, the field of land administration, highly dependent on 

reliable transactions and mutations, is a very suitable application field. Blockchain would also be applicable for 

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setting which depend on participatory processes. Therefore there is also a high potential of blockchain 

technologies for participatory processes, needed in spatial planning in general (Muth et al., 2019).  In a similar 

vein as blockchain, graph databases ironically address the problems of finding relations and correlations between 

data, which in relational databases are only possible by constructing the appropriate queries. As such, graph data 

structures are better capable dealing with unknown and hidden patterns and are more flexible in constructing 

and analysing relations.   

3.3. Current evidence of changes and disruptions in spatial planning and land management practices, 
regulations and agencies due to innovations in geospatial technologies  

There a clear difference between where which technologies have already a major impact and those 

technological advancements where the impact is still limited or being disputed. Clearly, advancing and 

integrating for the domain are the visualisation and modelling technologies. The connection of BIM with (open) 

GIS, and the CityGML models are not only fostering more accurate and up-to-date representations of the 

building environment, but also fostering a connection between architectural, planning and land management 

processes and professionals. This trend is visible through the increasing professional and scientific publications 

on 3D Cadastres, making use of the connection of BIM and GIS (Sun et al., 2019), and in the combination of 

housing permit or land use compliance activities (Altıntaş and Ilal, 2021). Furthermore. Insurance and 

construction companies are increasingly investing in BIM in combination with GIS as this combination can make 

both an assessment of the volumes and shapes of property assets, which are underlying the property value and 

possible loss assessments, as well as cater for possible evacuation routes.  Hence, fire disaster plans can rely on 

these technologies. Nevertheless, in practice the legal adoption of 3D cadastres using these technologies is still 

limited worldwide.  Paasch and Paulsson (2021) argue a clear and unambiguous legal definition of 3D property 

remains difficult. 

 The extended reality technologies are equally disruptive, as they provide entirely new cognitive 

experiences, real-life-like alternative scenarios for stakeholders in the spatial planning process. Datta (2019) 

predicts especially an uptake of these technologies in tourism, architecture and construction, but also foresees a 

realistic adoption in retails management and safety management. The Holocity example (Lock et al., 2019) shows 

how planners virtually wander through the city of Sydney and explore possible re-design alternatives 

interactively. Similarly, a virtual walk through a never built project of a century ago based on 92-year-old 

drawings interpreted and digitally recreated in Halle shows how one can experience alternative and timeless 

realities (Fuhrmann, 2021).  

The alternative modelling and data processing technologies such as the use of graph technologies and 

blockchain-based data handling  are also on the rise, and seem to be especially relevant for areas where there is 

a high need for large-volume and  reliable and transparent transactions. This applies in particular for  the land 

registration and land recordation functions (Ameyaw and de Vries, 2020; Bennett et al., 2020),  even though 

there are not many operational examples of where administrations truly rely on blockchain. The role of big data 

and big data analytics, combined with artificial intelligence and machine-learning algorithms is furthermore 

growing, especially in the activities of (automated) land use mapping, automated  monitoring and spatial (change, 

risk) assessments,  collaborative planning and community participation (de Vries, 2018b). 

Currently still disputed for one or more reasons are the embedding of digital twins in planning processes, 

the use of artificial intelligence for compliance and enforcement, and the veracity of big data. Regarding digital 

twins Marcucci et al. (2020) argue that in a planning process there must be an active role of planners and decision 

makers, which should at least be familiar with the basic tenets, functionalities benefits and limitations  of the 

technologies. As long as this is not the case, a full adoption in participatory planning and decision-making phases 

is still hampered.  This corresponds to the critique of Tomko and Winter (2019) among others, who argue that 

the metaphor of a digital twin seems to neglect a fundamental aspect in the digital environment, namely people 

and the cyber-social ecosystem connected to the cyber-physical ecosystem.  People can influence and alter both 

ecosystems, whilst being a passive or active change agent of it.       

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There are several discourses about the use of artificial intelligence in the context of compliance and 

enforcement. Whilst some applaud its use, for example for the managing and enforcement of conservation and 

maintaining public spatial restrictions (Fang et al., 2019) , others warn for certain types of misuse (Maas, 2019; 

Hoffmann-Riem, 2020) and the need for more ethical considerations (Georgiadou et al., 2020). Despite the fact 

that geospatial data are now directly uploaded through mobile platforms and active sensors, the mere existence 

of data does not necessarily produce a direct benefit. It still requires complex methodologies, continuous accuracy 

and validity feedback loops and some form of accountability checks to make these data meaningful, especially in 

a spatial governance context. Veracity and reliability are therefore still crucial issues, as well as informational 

privacy and human dignity. The compound word (geo) privacy suggests that the location of an individual does 

not only relate to traditional geographic coordinates, but can be inferred from people’s connotations, expressed 

interests, activities, and sociodemographic profiles.   

Finally, the actual adoption of the technologies in spatial planning and land management processes is still 

largely in an experimentation and testing phase. For example, the Bavarian Survey Authority (Bayerische 

Vermessungsverwaltung) do apply Lidar Measurements, and shifted from relying on stereoscopic pictures. 

Additionally, they apply their own BIM integrated GIS System achieving higher levels of Details for their digital 

maps and storages, and apply machine-learning algorithms in identifying newly built houses and constructions 

comparing two consecutive taken images of the same area. Nevertheless, final decisions on land use zoning, 

compliance and administration are still made by the human staff members. This also includes Big Data analytics.  

 

4. Conclusion 

The synthesis of documented evidence demonstrates that a broad range of geospatial methodologies, 

instruments and technologies exist, which the fields of spatial planning and land management are currently 

already employing. The uptake of GIS-based and image processing algorithms are especially evident for the 

functions of spatial monitoring and assessment and the administration of land and properties, but also for the 

functions of spatial structuring and design, coercion and compliance and participation and mobilisation for 

example agent-based modelling and the use of cellular automata are effectively used. Despite the significant 

advancements in planning and management capabilities, most of these technologies still have a number of 

problems. They are too rigid, too inflexible and lack capabilities of capturing and finding non-standard models, 

relations and uncertainties. In spatial planning and land management, and especially when dealing with dynamic 

stakes, interests and behaviour of people on the one hand, and complex ecological systems on the other, handling 

such dynamic uncertainties is crucial.       

The novel technologies, which are most likely to affect and possibly disrupt current functions and 

processes of spatial planning and land management, include machine-learning, LIDAR, BIM in connection with 

GIS, Blockchain, Big data analytics, Extended, immersive and mixed reality, different types of operational 

research and digital twins.  These technologies are better able to handle dynamic uncertainties and provide 

alternative access authorities. The prime advantages are faster and more accurate mining and analysis 

possibilities, decreased dependence on centralised storage of data, easier and more democratised access to 

analytical functions and algorithms and more automated integration of technologies and services. It must also 

be noted that despite its advantages, blockchain technology for example must never be a goal in itself for 

innovating land registration. Downside of this technology is also higher ecological footprint connected to its 

decentralised data storage, data processing and data volumes, and continued steep learning curves for 

practitioners.        

There is increasing evidence that spatial planning and land management practices, regulations and 

agencies are fundamentally changing because of the disruptive technologies. Active stakeholders such as 

construction companies, insurance companies, developers, building owners, municipalities, and professionals 

increasingly invest in BIM in connection with GIS for their 3D models, assessments, plans and developments 

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and hence increasingly rely on BIM with GIS for their business, private and/or public financial and economic 

decisions. Also, deep learning algorithms find a broadening set of application domains.  Whilst technologies keep 

on developing, there is an increasing need to reflect on the ethical dilemmas related to the technologies. Whereas 

technical professionals could previously always rely on relatively value-neutral technologies and technological 

products, issues such as uncontrolled automated judgments, surveillance, deep fake and (geo) privacy 

infringements are more at stake than ever. The legal and societal impacts are yet still relatively underrepresented 

in current research.      

 

5. Acknowledgements 

No funding was available for this research.  

 

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