A novel feature transform framework using deep neural network for multimodal floor plan retrieval

Sharma, Divya; Gupta, Nitin; Chattopadhyay, Chiranjoy; Mehta, Sameep

doi:10.1007/s10032-019-00340-1

Cited by 18 publications

(4 citation statements)

References 22 publications

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“…The 'Adam' optimization algorithm was used to train the algorithm [68]. Further details of DNN can be found in Cireşan et al [69], Yu et al [70], Kang et al [71], and Sharma et al [72].…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

Landslide Susceptibility Mapping with Deep Learning Algorithms

et al. 2022

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Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.

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“…The 'Adam' optimization algorithm was used to train the algorithm [68]. Further details of DNN can be found in Cireşan et al [69], Yu et al [70], Kang et al [71], and Sharma et al [72].…”

Section: Deep Neural Network (Dnn)mentioning

confidence: 99%

Landslide Susceptibility Mapping with Deep Learning Algorithms

et al. 2022

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“…al. [32] developed a machine learning approach that maps floor plan images or user sketches of floor plans to floor plan images in a database. An enhanced version of this approach ("FloorNet") was developed by [17] with the goal of making more accurate real estate recommendations for users on online real estate platforms.…”

Section: Image Analysis In Real Estatementioning

confidence: 99%

Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Solovev

Pröllochs

2021

Proceedings of the Web Conference 2021

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Online real estate platforms have become significant marketplaces facilitating users' search for an apartment or a house. Yet it remains challenging to accurately appraise a property's value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9,174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices -even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.

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“…Zeng et al [2] use a multi-task CNN with a roomboundary-attention mechanism for floor plan recognition and room type classification. Sharma et al [13] propose a conjunction of autoencoder, Cyclic GAN and CNN for mapping between domains and for retrieving similar floor plan image.…”

Section: Contributionmentioning

confidence: 99%

Generalizing Floor Plans Using Graph Neural Networks

Philipsen

2021

2021 IEEE International Conference on Image Processing (ICIP)

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The proliferation of indoor maps is limited by the manual process of generalizing floor plans. Previous attempts at automating similar processes use rasterization for structure. With Graph Neural Networks (GNN) it is now possible to skip rasterization and rely on the inherent structures in CAD drawings. A core component in floor plan generalization is localization of doors. We show how floor plan graphs can be extracted directly from CAD primitives and how state-ofthe-art GNNs can be used to classify graph nodes as door or non-door. Generalization is represented by the creation of placeholder bounding boxes using the labelled graph nodes. Our graph-based approach completely outperforms the Faster R-CNN baseline, which fail to locate any doors with the desired localization accuracy. To support further development of graph-based methods and comparison with raster-based methods, we publish a new dataset that consists of both image and graph-based floor plan representations.

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A novel feature transform framework using deep neural network for multimodal floor plan retrieval

Cited by 18 publications

References 22 publications

Landslide Susceptibility Mapping with Deep Learning Algorithms

Landslide Susceptibility Mapping with Deep Learning Algorithms

Integrating Floor Plans into Hedonic Models for Rent Price Appraisal

Generalizing Floor Plans Using Graph Neural Networks

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