Automatic Building Age Prediction from Street View Images

Sun, Maoran; Zhang, Fan; Duarte, Fábio

doi:10.1109/ic-nidc54101.2021.9660554

Cited by 3 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, comparing the accuracy with that achieved in the previous study demonstrated significant improvement over the results of using accurate Japanese real estate images with built-year and building-structure classifications of 0.367 and 0.786, respectively [17]. A previous study using SVI data achieved an accuracy of 0.869-0.871 in material classification in Chile [25] and an accuracy of 0.614 and 0.81 in built age classification in Austria and in Amsterdam, respectively [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30].…”

Section: B Classification Of the Built Year And Structure Of Individu...mentioning

confidence: 98%

“…When GIS building data and SVIs are combined for a highdensity area (such as an urban area), simply linking the nearest building and shooting point may not help to annotate the building accurately if the vehicle's travel direction and AOV are not considered. Previous studies used images recorded with the camera direction perpendicular to the travel direction, and this helped calculate the compass direction of each detected building easily based on its relative position in the image [25], [26], [27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40]. However, a 360°SVI in a high-density building area cannot correctly annotate the buildings with GIS building data because the buildings are densely packed.…”

Section: A Automated Annotation For the Development Of Training Datamentioning

confidence: 99%

“…In recent years, Sun et al [29], [30] proposed a built-year estimation model using SVIs and presented an automated workflow for estimating building age from SVIs because of the increasing coverage of SVIs, such as GSV. The proposed model was constructed considering a 30-year time slice without considering the earthquake resilience of buildings; the low accuracy in dense areas within urban areas is due to the image cropping method.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Ogawa

Zhao

Oki

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Section: B Classification Of the Built Year And Structure Of Individu...mentioning

confidence: 98%

Section: A Automated Annotation For the Development Of Training Datamentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Ogawa

Zhao

Oki

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

“…Zeppelzauer et al (2018) introduced the automated age estimation method for unconstrained facade photographs from patch-level visual feature learning to global age classification. Afterwards, Despotovic et al (2019), Sun et al (2021), and Ogawa et al (2023) continually explored possible building age estimation methods using deep learning techniques from facade views.…”

Section: Introductionmentioning

confidence: 99%

“…The chronological classification of this dataset covers modern buildings from 1969 to 2010, and this time span makes the chronological differentiation of building facade features less obvious. In another related work,Sun et al (2021) combined Google Street View data with a building age dataset from Amsterdam, aiming for a more detailed analysis.…”

mentioning

confidence: 99%

Zero-Shot Building Age Classification from Facade Image Using GPT-4

Zeng,

Goo,

Wang

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. A building’s age of construction is crucial for supporting many geospatial applications. Much current research focuses on estimating building age from facade images using deep learning. However, building an accurate deep learning model requires a considerable amount of labelled training data, and the trained models often have geographical constraints. Recently, large pre-trained vision language models (VLMs) such as GPT-4 Vision, which demonstrate significant generalisation capabilities, have emerged as potential training-free tools for dealing with specific vision tasks, but their applicability and reliability for building information remain unexplored. In this study, a zero-shot building age classifier for facade images is developed using prompts that include logical instructions. Taking London as a test case, we introduce a new dataset, FI-London, comprising facade images and building age epochs. Although the training-free classifier achieved a modest accuracy of 39.69%, the mean absolute error of 0.85 decades indicates that the model can predict building age epochs successfully albeit with a small bias. The ensuing discussion reveals that the classifier struggles to predict the age of very old buildings and is challenged by fine-grained predictions within 2 decades. Overall, the classifier utilising GPT-4 Vision is capable of predicting the rough age epoch of a building from a single facade image without any training. The code and dataset are available at https://zichaozeng.github.io/ba_classifier.

show abstract

Aerial-terrestrial data fusion for fine-grained detection of urban clues

Gosling-Goldsmith,

Antos,

Triveno

et al. 2024

Environment and Planning B: Urban Analytics and City Science

View full text Add to dashboard Cite

Those who work in the design, development, and management of cities are often limited by the scarcity of data. Particularly in the Global South, urban databases may be insufficient, out of date, or simply not available. However, digital technology is making it possible to fill gaps and build substantial datasets using “urban clues,” or attributes, gathered in high-resolution imagery by sky- and street-based cameras. Aided by machine learning, it is possible to detect specific building characteristics (purpose, condition, size, material, and construction)—yielding an array of geolocated details about the built environment. The resulting composite view can be made available, as we have done, in an open-source portal for use in urban management. The insights gained in this way may help address common urban management challenges, such as locating homes vulnerable to hazards such as flooding or earthquakes, identifying urban sprawl and informal housing, prioritizing infrastructure investments, and guiding public program support. This approach has been applied in Colombia, Guatemala, Indonesia, Mexico, Paraguay, Peru, St Lucia, and St Maarten.

show abstract

Automatic Building Age Prediction from Street View Images

Cited by 3 publications

References 28 publications

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Deep Learning Approach for Classifying the Built Year and Structure of Individual Buildings by Automatically Linking Street View Images and GIS Building Data

Zero-Shot Building Age Classification from Facade Image Using GPT-4

Aerial-terrestrial data fusion for fine-grained detection of urban clues

Contact Info

Product

Resources

About