Search Results (14)

Accurate object detection and measurement within indoor environments—particularly unfurnished or minimalistic spaces—pose unique challenges for conventional computer vision methods. Previous research has been limited to small objects that can be fully detected by applications such as YOLO, or to outdoor environments where reference elements are more abundant. However, in indoor scenarios with limited detectable references—such as walls that exceed the camera’s field of view—current models exhibit difficulties in producing complete detections and accurate distance estimates. This paper introduces a geometry-driven, redundancy-based framework that leverages proportional laws and architectural heuristics to enhance the measurement accuracy of walls and spatial divisions using standard smartphone cameras. The model was trained on 204 labeled indoor images over 25 training iterations (500 epochs) with augmentation, achieving a mean average precision (mAP@50) of 0.995, precision of 0.995, and recall of 0.992, confirming convergence and generalisation. Applying the redundancy correction method reduced distance deviation errors to approximately 10%, corresponding to a mean absolute error below 2% in the use case. Unlike depth-sensing systems, the proposed solution requires no specialised hardware and operates fully on 2D visual input, allowing on-device and offline use. The framework provides a scalable, low-cost alternative for accurate spatial measurement and demonstrates the feasibility of camera-based geometry correction in real-world indoor settings. Future developments may integrate the proposed redundancy correction with emerging multimodal models such as SpatialLM to extend precision toward full-room spatial reasoning in applications including construction, real estate evaluation, energy auditing, and seismic assessment. Full article

The emergence of Multimodal Large Language Models (MLLMs) has made methods of artificial intelligence accessible to the general public in a conversational way. It offers tools for the automated visual assessment of the quality of a built environment for professionals of urban planning without requiring specific technical knowledge on computing. We investigated the capability of MLLMs to perceive urban environments based on images and textual prompts. We compared the outputs of several popular models—ChatGPT, Gemini and Grok—to the visual assessment of experts in Architecture, Engineering and Construction (AEC) in the context of a real estate construction project. Our analysis was based on subjective attributes proposed to characterize various aspects of a built environment. Four urban identities served as case studies, set in a virtual environment designed using professional 3D models. We found that there can be an alignment between human and AI evaluation on some aspects such as space and scale and architectural style, and more general accordance in environments with vegetation. However, there were noticeable differences in response patterns between the AIs and AEC experts, particularly concerning subjective aspects such as the general emotional resonance of specific urban identities. It raises questions regarding the hallucinations of generative AI where the AI invents information and behaves creatively but its outputs are not accurate. Full article

This study examines the influence of sustainability certifications on the real estate market, particularly highlighting the advantages they offer compared to uncertified buildings and their recognition within the industry. A survey targeting various industry professionals garnered ninety responses, predominantly from the real estate sector. The survey explored the respondents’ awareness and perceived benefits of sustainability certifications, their priority areas within sustainability, and the relevance of these certifications across different real estate sectors. The analysis also compared the additional costs and operational savings of certified versus uncertified buildings. Among the certifications, LEED and BREEAM were the most recognized. The primary benefits associated with these certifications included enhanced corporate image, improved health and well-being, increased building value, and higher rental yields. We estimated a valuation and rent premium for certified buildings, noting that these premiums were more pronounced among respondents who were younger, had less professional experience, and were from the property sector. The office market was identified as the segment placing the highest importance on sustainability certifications. Additionally, the LiderA evaluation system’s weighting closely aligned with the respondents’ sustainability priorities. This study concludes that while sustainability certifications incur a cost premium, this is outweighed by the appreciation in building value, rental advantages, and operational cost savings. Full article

Keeping the real estate cadastre’s database up to date is a very important process. The scope of the modernization works includes, among other things, fieldwork and office (chamber) work carried out by surveyors to update information about land and buildings. Therefore, modernization may result in changes to plot areas; changes to the marking of plots and land use (and, consequently, a change in the property tax); and the disclosure or deletion of buildings and premises, as well as changes to their technical data. The research, based on a case study (rural municipality Serniki, Poland), and supported by a literature review, remote sensing, and digital photogrammetry, clearly showed the importance of initiating the cadastre modernization procedure and obtaining funds for this purpose, which will be beneficial for both parties. Landowners will gain by bringing the current image of their real estate closer to the actual state (e.g., by paying taxes for the real utility of the land), while administrative units will become the beneficiaries of higher tax revenues (up to over 500%). Thus, the analysis carried out on the case study shows positive effects for both parties, and justifies the financial outlay incurred by the administrative units for this process. Moreover, the analysis revealed that, due to the possibility of obtaining funding from other sources, the cost to the public administration may be marginal. Thus, the cadastre modernization procedure should be integrated into regional and national policies. Full article

Renewable energy sources provide an important solution in environmental protection activities and in the process of shaping sustainable development. The search for optimal locations enabling full exploitation of the energy intensity of real estate presents a significant challenge in terms of geoinformation analysis methods in a GIS environment. The aim of the study was to develop a capabilities matrix for the location of photovoltaic farms and, based on this, to compile a map of decision alternatives for these locations. The first stage involved the determination of the spatial features (stimulants and destimulants), which were significant in the context of photovoltaic (PV) farm location. During the analysis, the scope of the necessary data and their sources, which included topographic vector studies, aerial images, and the OpenStreetMap open data, were determined. The next stage was to determine the weights of the features which were significant in the context of photovoltaic (PV) farm location. To this end, the Multicriteria Decision Making (MCDM) method, including the Analytic Hierarchy Process (AHP) method, was employed. For the verification of the results, the entropy measure was also used. Entropy was calculated based on the diversity of previously identified geospatial features that shape the optimum conditions for their location, based on the photovoltaic farms already existing in Poland. A total of 555 photovoltaic farms were analysed. The next stage assumed the performance of geoinformation analyses using GIS tools and the development of a capabilities matrix for the PV farm location for the selected commune in Poland. The final stage involved the compilation of a PV decision alternative map for the selected commune based on the capabilities matrix. As a result, as an example, a ranking of plots was developed as decision-making alternatives for the municipality of Czarnia located in the northeastern part of Poland. It shows which parcels of land primarily have the dimension of spatial features that are favourable for the location of PV. More than 6900 parcels were analysed, in which 176 presented the highest value of the index of optimal PV location generated using the AHP method. This method provides a basis for further work by identifying optimal locations taking into account existing spatial conditions. The analyses carried out can be an important document in the future for spatial management, in particular for the location of new PV farms. As a continuation of the research, the authors will work on expanding the scope of the analyses and automating the entire process. Full article

This paper presents the findings of a study on the impact of the transformation of a road transport corridor on the form of a functio-spatial structure, as determined by the placement of significant economic activity sites within this corridor. The investigation of relations and interdependencies in the development of the road transport system and development structure transformation processes allowed for the identification of tendencies in the shaping of space, as well as for building models that reflect the transformation of the road corridor under study. The study focused on a fragment of the road transport corridor between Kraków and Bochnia (called the “Kraków East–Bochnia” corridor), as a distinctive case of contemporary transformations of the functio-spatial structure that happen under the influence of the construction and opening of a section of highway A4. The study was based on original field work, a review of the literature and an analysis of applicable planning documents. GIS tools, cartographic resources and satellite images were also used. The transformation of the area under investigation and the increase in its accessibility due to the presence of the highway pointed to the area’s high attractiveness in terms of real estate development—especially at nodal sites along the linkages that connected the highway with other elements of the road corridor. The relationships between the completed highway section and the previous road layout are crucial to the emergence of economic activity sites and areas, and thus lead to a transformation of developed space following a new spatial model. Full article

Automatic prediction of the aesthetic value of images has received increasing attention in recent years. This is due, on the one hand, to the potential impact that predicting the aesthetic value has on practical applications. Even so, it remains a difficult task given the subjectivity and complexity of the problem. An image aesthetics assessment system was developed in recent years by our research group. In this work, its potential to be applied in commercial tasks is tested. With this objective, a set of three portals and three real estate agencies in Spain were taken as case studies. Images of their websites were taken to build the experimental dataset and a validation method was developed to test their original order with another proposed one according to their aesthetic value. So, in this new order, the images that have the high aesthetic score by the AI system will occupy the first positions of the portal. Relevant results were obtained, with an average increase of 52.54% in the number of clicks on the ads, in the experiment with Real Estate portals. A statistical analysis prove that there is a significant difference in the number of clicks after selecting the images with the AI system. Full article

Based on a newly adopted “Rulebook on the records of identified changes on buildings in Serbia” (2020) that regulates the content, establishment, maintenance and use of records on identified changes on buildings, it is expected that the geodetic-cadastral information system will be extended with these records. The records contain data on determined changes of buildings in relation to the reference epoch of aerial or satellite imagery, namely data on buildings: (1) that are not registered in the real estate cadastre; (2) which are registered in the real estate cadastre, and have been changed in terms of the dimensions in relation to the data registered in the real estate cadastre; (3) which are registered in the real estate cadastre, but are removed on the ground. For this purpose, the LADM-based cadastral data model for Serbia is extended to include records on identified changes on buildings. In the year 2020, Republic Geodetic Authority commenced a new satellite acquisition for the purpose of restoration of official buildings registry, as part of a World Bank project for improving land administration in Serbia. Using this satellite imagery and existing cadastral data, we propose a method based on comparison of object-based and pixel-based image analysis approaches to automatically detect newly built, changed or demolished buildings and import these data into extended cadastral records. Our results, using only VHR images containing only RGB and NIR bands, showed object identification accuracy ranging from 84% to 88%, with kappa statistic from 89% to 96%. The accuracy of obtained results is satisfactory for the purpose of developing a register of changes on buildings to keep cadastral records up to date and to support activities related to legalization of illegal buildings, etc. Full article

This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future. Full article

The paper investigates the urban landscape changes for the last 50 years in Bucharest, the capital city of Romania. Bucharest shows a complex structural transformation driven by the socialist urban policy, followed by an intensive real-estate market development. Our analysis is based on a diachronic set of high-resolution satellite imagery: declassified CORONA KH-4B from 1968, SPOT-1 from 1989, and multisensor stacked layers from Sentinel-1 SAR together with Sentinel-2MSI from 2018. Three different datasets of land cover/use are extracted for the reference years. Each dataset reveals its own urban structure pattern. The first one illustrates a radiography of the city in the second part of the 20th century, where rural patterns meet the modern ones, while the second one reveals the frame of a city in a full process of transformation with multiple constructions sites, based on the socialist model. The third one presents an image of a cosmopolitan city during an expansion process, with a high degree of landscape heterogeneity. All the datasets are included in a built-up change analysis in order to map and assess the spatial transformations of the city pattern over 5 decades. In order to quantify and map the changes, the Built-up Change Index (BCI) is introduced. The results highlight a particular situation linked to the policy development visions for each decade, with major changes of about 50% for different built-up classes. The GIS analysis illustrates two major landscape transformations: from the old semirural structures with houses surrounded by gardens from 1968, to a compact pattern with large districts of blocks of flats in 1989, and a contemporary city defined by an uncontrolled urban sprawl process in 2018. Full article

Urban sprawl has been studied principally as a phenomenon produced by a lack of or weakness in urban planning, as a consequence of real estate liberalization. This article examines the Chilean case, and proposes that the state has been the engine of this phenomenon through spatial planning instruments that have both neoliberal and neostructural features, and that are best defined by the concept, new public management. The analysis tracks urban sprawl in four intermediate cities, which have experienced high rates of growth since 2000, using photointerpretation of satellite images between 2003 and 2011, and the creation of a typology to define land uses and housing types. The results show that intermediate cities follow similar trends to the capital city, Santiago, and face similar problems, in particular the concentration of services in the urban core. These similarities are produced by the application of general planning instruments: Article 55 and Decree Law 3516. While most research on urban sprawl focuses on private agency, this article highlights the role of the state in its production. It is therefore relevant to explore the nature of public agency in urban sprawl processes in different metropolitan and intermediate cities, and how planning policies can be adapted to curb the phenomenon. Full article

Land use and land cover changes (LULCC) are prime variables that reflect changes in ecological systems. The Guangdong, Hong Kong, and Macau (GHKM) region located in South China has undergone rapid economic development and urbanization over the past three decades (1986–2017). Therefore, this study investigates the changes in LULC of GHKM based on multi-year Landsat and nighttime light (NTL) data. First, a supervised classification technique, i.e., support vector machine (SVM), is used to classify the Landsat images into seven thematic classes: forest, grassland, water, fishponds, built-up, bareland, and farmland. Second, the demographic activities are studied by calculating the light index, using nighttime light data. Third, several socioeconomic factors, derived from statistical yearbooks, are used to determine the impact on the LULCC in the study area. The post-classification change detection shows that the increase in the urban area, from 0.76% (1488.35 km²) in 1986 to 10.31% (20,643.28 km²) in 2017, caused GHKM to become the largest economic segment in South China. This unprecedented urbanization and industrialization resulted in a substantial reduction in both farmland (from 53.54% (105,123.93 km²) to 33.07% (64,932.19 km²)) and fishponds (from 1.25% (2463.35 km²) to 0.85% (1674.61 km²)) during 1986–2017. The most dominant conversion, however, was of farmland to built-up area. The subsequent urban growth is also reflected in the increasing light index trends revealed by NTL data. Of further interest is that the overall forest cover increased from 33.24% (65,257.55 km²) to 45.02% (88,384.19 km²) during the study period, with a significant proportion of farmland transformed into forest as a result of different afforestation programs. An analysis of the socioeconomic indicators shows that the increase in gross domestic product, total investment in real estate, and total sales of consumer goods, combined with the overall industrialization, have led to (1) urbanization on a large scale, (2) an increased light index, and (3) the reduction of farmland. The speed of development suggests that opportunistic development has taken place, which requires a pressing need to improve land policies and regulations for more sustainable urban development and protection of farmland. Full article

Latin American (LA) megacities are facing enormous challenges to provide welfare to millions of people who live in them. High rates of urbanization and limited administrative capacity of LA cities to plan and control urban growth have led to a critical deficit of urban green space, and therefore, to sub-optimal outcomes in terms of urban sustainability. This study seeks to assess the possibility of using real estate prices to provide an estimate of the monetary value of the ecosystem services provided by urban green space across five Latin American megacities: Bogota, Buenos Aires, Lima, Mexico City and Santiago de Chile. Using Google Earth images to quantify urban green space and multiple regression analysis, we evaluated the impact of urban green space, crime rates, business density and population density on real estate prices across the five mentioned megacities. In addition, for a subset of the data (Lima and Buenos Aires) we analyzed the effects of landscape ecology variables (green space patch size, connectivity, etc.) on real estate prices to provide a first insight into how the ecological attributes of urban green space can determine the level of ecosystem service provision in different urban contexts in Latin America. The results show a strong positive relationship between the presence of urban green space and real estate prices. Green space explains 52% of the variability in real estate prices across the five studied megacities. Population density, business density and crime had only minor impacts on real estate prices. Our analysis of the landscape ecology variables in Lima and Buenos Aires also show that the relationship between green space and price is context-specific, which indicates that further research is needed to better understand when and where ecological attributes of green space affect real estate prices so that managers of urban green space in LA cities can optimize ecological configuration to maximize ecosystem service provision from often limited green spaces. Full article

Place is seen as a process whereby social and cultural forms are reproduced. This process is closely linked to capital flows, which are, in turn, shaped by changing property regimes. However, relatively little attention has been paid to the relationship between property regimes, capital flows and place-making. The goal of this paper is to highlight the role of changing property regimes in the production of place. Our research area is South Luogu Lane (SLL) in Central Beijing. We take elites’ former houses in SLL as the main unit of analysis in this study. From studying this changing landscape, we draw four main conclusions. First, the location of SSL was critical in enabling it to emerge as a high-status residential community near the imperial city. Second, historical patterns of capital accumulation influenced subsequent rounds of private investment into particular areas of SLL. Third, as laws relating to the ownership of land and real estate changed fundamentally in the early 1950s and again in the 1980s, the target and intensity of capital flows into housing in SLL changed too. Fourth, these changes in capital flow are linked to ongoing changes in the place image of SLL. Full article