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Accelerating Urban Modelling Algorithms with Artificial Intelligence
2019
Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management
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In this paper, we demonstrate that developments in computer hardware to support the increasingly complex artificial intelligence workflows for Deep Learning networks can be adapted for urban modelling ...
The starting point for this paper is a 3D visualisation of the Queen Elizabeth Olympic Park, developed using a web-based spatial interaction modelling system which calculates population metrics on the ...
Their analysis of the types of functions that deep neural networks can be made to approximate is important in the context of modelling when the function underlying the observed data is under investigation ...
doi:10.5220/0007727201050116
dblp:conf/gistam/MiltonR19
fatcat:c4bhl32ne5aiphxmlxioh4vsau
Urban Zoning Using Higher-Order Markov Random Fields on Multi-View Imagery Data
[chapter]
2018
Lecture Notes in Computer Science
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In the proposed HO-MRF, top-view satellite data is segmented via a multi-scale deep convolutional neural network (MS-CNN) and used in lowerorder potentials. ...
Urban zoning enables various applications in land use analysis and urban planning. As cities evolve, it is important to constantly update the zoning maps of cities to reflect urban pattern changes. ...
In our HO-MRF model, unary terms are computed from visual features extracted on the satellite image S via a deep convolutional neural network. ...
doi:10.1007/978-3-030-01237-3_38
fatcat:b7mokyzqn5cbvi6yoyyrrrmgjm
AI and Deep Learning for Urban Computing
[chapter]
2021
The Urban Book Series
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Finally, we discuss the applications of urban computing including urban planning, urban transportation, location-based social networks (LBSNs), urban safety and security, and urban-environment monitoring ...
Thus, we briefly introduce the deep-learning models that are widely used in various urban-computing tasks. ...
Then, the functions of each region are inferred by a proposed graphic-based probabilistic inference model. ...
doi:10.1007/978-981-15-8983-6_43
fatcat:uq7j3hvsvzfl5lq33omx64un3i
A novel method of predictive collision risk area estimation for proactive pedestrian accident prevention system in urban surveillance infrastructure
[article]
2021
arXiv
pre-print
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The proposed system applied trajectories of vehicles and pedestrians from video footage after preprocessing, and then predicted their trajectories by using deep LSTM networks. ...
A breakthrough for proactively preventing pedestrian collisions is to recognize pedestrian's potential risks based on vision sensors such as CCTVs. ...
In this section, we describe the proposed trajectory prediction model based on deep LSTM networks, an evolution of recurrent neural network (RNN). ...
arXiv:2105.02572v1
fatcat:c2uzm4wmwrh3jmyxsi36tgwdhe
Urban sprawl assessment and modeling using landsat images and GIS
2016
Modeling Earth Systems and Environment
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This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system ...
Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. ...
ANFIS was first proposed by Jang (1993) based on the Takagi-Sugeno model based fuzzy inference systems and neural network structures (Jang 1993) . ...
doi:10.1007/s40808-016-0209-4
fatcat:qpuhb7ezifhllcek3u5php7w4q
A semi-supervised deep residual network for mode detection in Wi-Fi signals
[article]
2019
arXiv
pre-print
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In this study, we develop a semi-supervised deep residual network (ResNet) framework to utilize Wi-Fi communications obtained from smartphones for the purpose of transportation mode detection. ...
This framework is evaluated on data collected by Wi-Fi sensors located in a congested urban area in downtown Toronto. ...
In this study, we develop a semi-supervised deep residual neural network for Wi-Fi signals to infer mode of transportation of network users in a congested urban area in Downtown Toronto. ...
arXiv:1902.06284v1
fatcat:yntlonipbffflcbzfbs3ebnlz4
A Semi-Supervised Deep Residual Network for Mode Detection in Wi-Fi Signals
2020
Journal of Big Data Analytics in Transportation
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In this study, by a passive collection of Wi-Fi network data on a congested urban road in downtown Toronto, we attempt to tackle the aforementioned problems. ...
Inferring transportation mode of users in a network is of paramount importance in planning, designing, and operating intelligent transportation systems. ...
In this study, we develop a semi-supervised deep residual neural network for Wi-Fi signals to infer the mode of transportation of network users in a congested urban area in Downtown Toronto. ...
doi:10.1007/s42421-020-00022-z
fatcat:e2vxut4pwfatzb554guusalkzy
IDENTIFICATION OF FOOD INSECURE ZONES USING REMOTE SENSING AND ARTIFICIAL INTELLIGENCE TECHNIQUES
2018
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In this study, a neural network approach is employed to identify the zones that ensure less access to food using indicators which mainly focuses on child population below five years, hunger index measuring ...
in risk of malnutrition and hunger. ...
The classification of urban aerial data based on pixel labelling with deep convolutional neural networks and logistic regression is adapted to the high resolution aerial images for feature extraction and ...
doi:10.5194/isprs-archives-xlii-5-659-2018
fatcat:eqrjpmwa6ncevb37dzrxngvq6i
Estimating Building Age from Google Street View Images Using Deep Learning (Short Paper)
2018
International Conference Geographic Information Science
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This paper proposes a novel method to estimate building age based on the convolutional neural network for image features extraction and support vector machine for construction year regression. ...
Building databases are a fundamental component of urban analysis. However such databases usually lack detailed attributes such as building age. ...
Several popular deep learning methods have been developed for image analysis. Convolutional neural network (CNN) is the state-of-art method for feature extraction [10] . ...
doi:10.4230/lipics.giscience.2018.40
dblp:conf/giscience/LiCRKA18
fatcat:6unsfvabqjbwjpmif4kg3fi46y
Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
2021
Mobile Information Systems
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In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. ...
In fact, numerous researchers have done massive work on scrutinizing the effectiveness of existing path loss models for channel modeling. ...
as shown in Figure 4 . e FIS approximate functions are based on a rule base, a database, and a reasoning mechanism. e adaptive neuro-fuzzy inference system (ANFIS) is a class of adaptive networks that ...
doi:10.1155/2021/6619364
fatcat:mx6o6rfsqbhpfnuqcedtpunrwm
Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. ...
Especially, we look into fusion based architectures and coarseto-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks. ...
Convolutional Neural Networks (CNN) for land use classification [4] or Fully Convolutional Networks (FCN) for semantic labeling of urban areas [1] . ...
doi:10.1109/cvprw.2017.199
dblp:conf/cvpr/AudebertSL17
fatcat:yub7f6rsxnadrcj4l2qbzxy6o4
Using Fractal Neural Networks to Play SimCity 1 and Conway's Game of Life at Variable Scales
[article]
2020
arXiv
pre-print
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To make our models compatible with variable-scale gameplay, we use Neural Networks with recursive weights and structure -- fractals to be truncated at different depths, dependent upon the size of the gameboard ...
of city-wide metrics, on gameboards of various sizes. ...
Conversely, the fact that one layer of weights can be repeatedly copied to create deep, trainable networks suggests an interesting avenue for Neuroevolution. ...
arXiv:2002.03896v1
fatcat:dyeboer3d5czlceh432dotxqaa
A SOE-Based Learning Framework Using Multi-Source Big Data for Identifying Urban Functional Zones
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Lastly, RF (random forest) is used to identify functional zones based on SOE features. ...
in determining functionality of urban zones. ...
Second, a novel ensemble learning model is introduced to identify urban functional zones, combining DCNN (Deep Convolutional Neural Network) with traditional classifier models. ...
doi:10.1109/jstars.2021.3091848
fatcat:rzggvni34bbwliufba6zsd5b74
Cyclic Graph Attentive Match Encoder (CGAME): A Novel Neural Network For OD Estimation
[article]
2022
arXiv
pre-print
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inference problem thus a new deep learning architecture is needed. ...
The proposed model achieves state-of-the-art compared with baselines in the designed experiments and offers a paradigm for inference tasks across representation space. ...
Acknowledgments The authors acknowledge support from the Center of High Performance Computing, Tsinghua University. ...
arXiv:2111.14625v4
fatcat:hszf6bpfqbfj5crshin6qnz3li
SideInfNet: A Deep Neural Network for Semi-Automatic Semantic Segmentation with Side Information
[article]
2020
arXiv
pre-print
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Inspired by the practicality and applicability of the semi-automatic approach, this paper proposes a novel deep neural network architecture, namely SideInfNet that effectively integrates features learnt ...
To evaluate our method, we applied the proposed network to three semantic segmentation tasks and conducted extensive experiments on benchmark datasets. ...
[25] proposed a model for fusing multi-view imagery data into a deep neural network for estimating geospatial functions land cover and land use. ...
arXiv:2002.02634v4
fatcat:eafyqwatyrdvllvvus56hj5umi
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