SURVEY TEKNIK PENGKLASIFIKASIAN GAYA ARSITEKTUR PADA FASAD BANGUNAN MENGGUNAKAN PENDEKATAN DEEP LEARNING CNN

Teknik pengklasifikasian gaya arsitektur pada fasad bangunan menjadi bagian penting pada dunia perancangan, guna mempercepat proses dalam melakukan kajian tipologinya. Dewasa ini dengan semakin berkembangnya teknologi informasi, sangat memungkinkan bila seiring waktu dengan berbagai kemajuan metode dalam mengekstraksi obyek bangunan utamanya fasad bangunan. Penelitian dalam pengklasifikasian fasad bangunan banyak dilakukan untuk menelusuri jenis bangunan maupun aspek estetika lainnya. Demi tujuan tersebut studi survey ini dimaksudkan untuk mengetahui teknik komputasi Deep Learning (DL) yang dapat digunakan dalam mengidentifikasi fasad bangunan secara lebih akurat dengan membedakan dan mengelompokkannya agar lebih mudah dikenali tipe bangunannya. Metode yang digunakan dalam melakukan penelitian ini menggunakan teknik seleksi dan eliminasi, berasal dari penelitian di berbagai jurnal yang relevan terhadap pengklasifikasian gaya arsitektur bangunan. Hasil survey literatur menunjukkan bahwa terdapat  kesenjangan, hasil akurasi dari yang tertinggi ke terendah sebesar 48,19 % sehingga diperlukan adanya inovasi pada perangkat sistemnya. Teknik DL paling banyak digunakan dengan pendekatan Convolutional Neural Network (CNN) yang dikombinasikan dengan sistem perangkat lain, daripada fiturnya sendiri guna meningkatkan nilai akurasi

Architectural style classification of Mexican historical buildings using deep convolutional neural networks and sparse features

International audienceWe propose a convolutional neural network to classify images of buildings using sparse features at the network’s input in conjunction with primary color pixel values. As a result, a trained neuronal model is obtained to classify Mexican buildings in three classes according to the architectural styles: prehispanic, colonial, and modern with an accuracy of 88.01%. The problem of poor information in a training dataset is faced due to the unequal availability of cultural material. We propose a data augmentation and oversampling method to solve this problem. The results are encouraging and allow for prefiltering of the content in the search tasks

Graph machine learning approaches to classifying the building and ground relationship Architectural 3D topological model to retrieve similar architectural precendents

Architects struggle to choose the best form of how the building meets the ground and may benefit from a suggestion based on precedents. A precedent suggestion may help architects decide how the building should meet the ground. Machine learning (ML), as a part of artificial intelligence (AI), can play a role in the following scenario to determine the most appropriate relationship from a set of examples provided by trained architects. A key feature of the system involves its classification of three-dimensional (3D) prototypes of architectural precedent models using a topological graph instead of two-dimensional (2D) images to classify the models. This classified model then predicts and retrieves similar architecture precedents to enable the designer to develop or reconsider their design. The research methodology uses mixed methods research. A qualitative interview validates the taxonomy collected in the literature review and image sorting survey to study the similarity of human classification of the building and ground relationship (BGR). Moreover, the researcher leverages the use of two primary technologies in the development of the BGR tool. First, a software library enhances the representation of 3D models by using non-manifold topology (Topologic). The second phase involves an end-to-end deep graph convolutional neural network (DGCNN). This study employs a two-stage experimental workflow. The first step sees a sizable synthetic database of building relationships and ground topologies created by generative simulation for a 3D prototype of architectural precedents. These topologies then undergo conversion into semantically rich topological dual graphs. Second, the prototype architectural graphs are imported to the DGCNN model for graph classification. This experiment's results show that this approach can recognise architectural forms using more semantically relevant and structured data and that using a unique data set prevents direct comparison. Our experiments have shown that the proposed workflow achieves highly accurate results that align with DGCNN’s performance on benchmark graphs. Additionally, the study demonstrates the effectiveness of using different machine learning approaches, such as Deep Graph Library (DGL) and Unsupervised Graph Level Representation Learning (UGLRL). This research demonstrates the potential of AI to help designers identify the topology of architectural solutions and place them within the most relevant architectural canons

Automatic semantic and geometric enrichment of CityGML 3D building models of varying architectural styles with HOG-based template matching

While the number of 3D geo-spatial digital models of buildings with cultural heritage interest is burgeoning, most lack semantic annotation that could be used to inform users of mobile and desktop applications about the architectural features and origins of the buildings. Additionally, while automated reconstruction of 3D building models is an active research area, the labelling of architectural features (objects) is comparatively less well researched, while distinguishing between different architectural styles is less well researched still. Meanwhile, the successful automatic identification of architectural objects, typified by a comparatively less symmetrical or less regular distribution of objects on façades, particularly on older buildings, has so far eluded researchers. This research has addressed these issues by automating the semantic and geometric enrichment of existing 3D building models by using Histogram of Oriented Gradients (HOG)-based template matching. The methods are applied to the texture maps of 3D building models of 20th century styles, of Georgian-Regency (1715-1830) style and of the Norman (1066 to late 12th century) style, where the amalgam of styles present on buildings of the latter style necessitates detection of styles of the Gothic tradition (late 12th century to present day). The most successful results were obtained when applying a set of heuristics including the use of real world dimensions, while a Support Vector Machine (SVM)-based machine learning approach was found effective in obviating the need for thresholds on matchscores when making detection decisions