Cumincad : CUMINCAD References : Search Results

CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

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	Audebert, N, Le Saux, B and Lef?vre, S (2018) Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks , ISPRS Journal of Photogrammetry and Remote Sensing, 140, pp. 20-32

	Battaglia, PW, et al. (2018) Relational inductive biases, deep learning, and graph networks , arXiv preprint arXiv:1806.01261, 1806, pp. 1-40

	Bayer, J., Bukhari, S. & Dengel, A. (2018) Interactive Design Support for Architecture Projects During Early Phases Based on Recurrent Neural Networks , ICPRAM 2018

	Bielik, M, Koenig, R, Schneider, S and Varoudis, S (2018) Measuring the Impact of Street Network Configuration on the Accessibility to People and Walking Attractors , Networks and Spatial Economics, p. 18 (3)

	Bielik, M. et al. (2018) Measuring the Impact of Street Network Configuration on the Accessibility to People and Walking Attractors, Networks and Spatial Economics, 18(3), pp , Networks and Spatial Economics, 18(3), pp. 657-676

	Bielik, M., Schneider, S., Kuliga, S., Griego, D., Ojha, V., König, R., Schmitt, G. and Donath, D. (2018) Measuring the Impact of Street Network Configuration on the Accessibility to People and Walking Attractors , Networks and Spatial Economics, 18, p. xx

	Bittner, K, Adam, F, Cui, S, KÃ¶rner, M and Reinartz, P (2018) Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks , IEEE Journal of Selected Topics in Applied EarthObservations and Remote Sensing, 11, p. 2615-2629

	Bittner, K, Adam, F, Cui, S, Körner, M and Reinartz, P (2018) Building Footprint Extraction From VHR Remote Sensing Images Combined With Normalized DSMs Using Fused Fully Convolutional Networks , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, p. 2615-2629

	Boeing, G. (2017) OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks , Computers, Environment and Urban Systems, 65, 126–139. https://doi.org/10.1016/j.compenvurbsys.2017.05.004Chestnut, J. (2018, March 28). Pedestrians first: Tools for a Walkable City. Institute for Transportation and Development Policy. Retrieved February 3, 2022, from https://www.itdp.org/publication/walkability-tool/

	Caye Daudt, R., Le Saux, B., & Boulch, A. (2018) Fully convolutional siamese networks for change detection , 2018 25th IEEE International Conference on Image Processing (ICIP), 4063-4067. https://doi.org/10.1109/ICIP.2018.8451652

	Chen, Wei, Fangzhou Guo, Dongming Han, Jacheng Pan, Xiaotao Nie, Jiazhi Xia, and Xiaolong Zhang (2018) Structure-Based Suggestive Exploration: A New Approach for Effective Exploration of Large Networks , IEEE Transactions on Visualization and Computer Graphics25 (1): 555–565

	Chen, Y, Gao, W, Widyaningrum, E, Zheng, M and Zhou, K (2018) Building classification of VHR airborne stereo images using fully convolutional networks and free training samples , Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., p. 87-92

	Creswell A, White T and Vincent D, et al (2018) Generative adversarial networks: An overview , IEEE Signal Processing Magazine 2018; 35(1): 53–65.

	Creswell A, White T, Dumoulin V, et al (2018) Generative adversarial networks: An overview , IEEE Signal Process Mag 2018; 35(1): 53–65.

	Daniel, P. & Burns, L. (2018) How steep is that street? Mapping 'real' pedestrian catchments by adding elevation to street networks , Radical Statistics,121, 26-48

	Ergan, Semiha & Radwan, Ahmed & Zou, Zhengbo & Tseng, Hua-an & Han, Xue. (2018) Quantifying Human Experience in Architectural Spaces with Integrated Virtual Reality and Body Sensor Networks , Journal of Computing in Civil Engineering. 33. 10.1061/(ASCE)CP.1943-5487.0000812

	Fujita, M., Ishido, K., Inoue, H., & Terano, T. (2018) Evaluating Researchers Through Betweenness Centrality Measures of Co-author Networks from Academic Literature Database: Finding Gatekeeper Researchers in Organizational Research , 218 IEEE International Conference on Big Data (Big Data), doi: 1.119/BigData.218.8622311

	Grekousis, G. (2019) Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis , Computers, Environment and Urban Systems, 74, 244-256. https://doi.org/10.1016/j.compenvurbsys.2018.10.008

	Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018) Social gan: Socially acceptable trajectories with generative adversarial networks , Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2255-2264)

	Huang, W. & Zheng. H. (2018) Understanding and Visualizing Generative Adversarial Networks in Architectural Drawings , 23rd International Conference on Computer-Aided Architectural Design Research Asia: Learning, Prototyping and Adapting, CAADRIA 2018 (pp. 156-165). The Association for Computer-Aided Architectural Design Research Asia (CAADRIA)

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